Toxic Memes Recognition Through with Multimodal Bidirectional Cross-Attention

1. Introduction

The pervasive influence of the internet has fundamentally transformed various facets of our daily existence, shaping not only how we communicate and access information but also reflecting and reinforcing our personal identities, beliefs, and, unfortunately, our inherent biases and prejudices. In today’s digital age, billions of individuals engage with a vast array of online content every single day. This content spans a broad spectrum, encompassing highly informative and educational materials that significantly enhance our knowledge and understanding of the world around us. However, alongside these beneficial resources, there is a growing surge in harmful content that poses substantial risks to individuals and communities alike. This detrimental content includes, but is not limited to, hate speech, the dissemination of misinformation, and various other forms of online abuse that can have severe psychological and societal impacts [3,4].

As the volume of harmful online content continues to escalate, the demand for effective detection and mitigation strategies becomes increasingly urgent. The rapid proliferation of such content necessitates the deployment of advanced technological solutions capable of swiftly identifying and addressing these issues. This involves scaling up content review processes and developing automated systems that can make real-time decisions to remove or flag harmful media. The primary objective is to minimize the potential harm inflicted upon readers by ensuring that offensive and abusive content is promptly taken down, thereby fostering a safer and more respectful online environment [8].

A significant portion of our online interactions transpires on social media platforms, which serve as conduits for sharing messages, images, and a multitude of multimedia content with both private communities and the general public. These platforms have become integral to modern communication, allowing for the instantaneous exchange of information and ideas across the globe. However, the very features that make social media platforms so effective for communication also make them susceptible to misuse. The ease of sharing content can be exploited to propagate hateful memes—multimodal content that combines images and text to convey messages that may be offensive or harmful [4].

In response to this challenge, the task of hateful meme recognition has been proposed, aimed at enhancing the detection of hateful memes that integrate both visual and textual elements. To facilitate this endeavor, the competition organizers have curated a unique and meticulously labeled dataset comprising over 10,000 high-quality multimodal memes [4]. This dataset serves as a critical resource for developing and benchmarking algorithms designed to identify hate speech within the intricate interplay of images and text that characterize memes. The primary goal of this competition is to devise an algorithm capable of accurately detecting multimodal hate speech in internet memes while maintaining robustness against benign alterations.

A meme can be classified as hateful or offensive based on its visual content, the accompanying text, or the synergistic combination of both. The concept of benign flipping plays a pivotal role in this context. Benign flipping is an augmentation technique employed by the competition organizers to invert the hateful classification of a meme, effectively transforming it from hateful to non-hateful or vice versa. This process involves altering either the textual component or the visual imagery of the meme, thereby changing its overall sentiment and impact. By incorporating benign flipping, the competition ensures that the developed algorithms are not only adept at identifying hate speech but are also resilient to subtle modifications that may otherwise obscure or alter the meme’s intent [4].

The detection of hateful memes is framed as a binary classification task, wherein each meme is categorized as either hateful or non-hateful. The primary metric for evaluating the performance of classification models in this task is the Area Under the Receiver Operating Characteristic Curve (AUROC). The AUROC quantifies the model’s ability to discriminate between the two classes by measuring the area under the Receiver Operating Characteristic (ROC) curve. The ROC curve itself plots the True Positive Rate (TPR) against the False Positive Rate (FPR) across various classification thresholds T. The objective is to maximize the AUROC, thereby enhancing the model’s discriminative power and ensuring it can effectively distinguish between hateful and non-hateful content under varying conditions [16].

$$\mathrm{AUROC}={\int }_{-\infty }^{\infty }\mathrm{TPR}\left(T\right)d\left(\mathrm{FPR}\left(T\right)\right)$$

In addition to AUROC, Accuracy serves as a supplementary metric to evaluate the performance of the classification models. Accuracy measures the proportion of correctly predicted instances out of the total number of instances in the test set. It is calculated as the ratio of the number of correct predictions $\widehat{y}$ to the actual class labels y:

$$\mathrm{Accuracy}={\displaystyle \frac{1}{N}}\sum _{i=1}^{N}I\left(y_i={\widehat{y}}_i\right)$$

The ideal model aims to optimize both AUROC and Accuracy, ensuring high discriminative capability and reliable classification performance.

In this study, we present a novel framework named BCA-MemeNet (Bidirectional Cross-Attention MemeNet), which introduces significant advancements in the detection of hateful memes through sophisticated multimodal deep learning techniques. Our contributions to the field are threefold:

• We undertake a comprehensive evaluation and fine-tuning of both single-stream and dual-stream state-of-the-art Transformer architectures, including VL-BERT [9], VLP [13], UNITER [2], and LXMERT [11]. By meticulously comparing these models against established baselines, we leverage their extensive pre-training on diverse datasets [8,10] to enhance their applicability and performance in the context of hateful meme detection.
• We introduce a novel bidirectional cross-attention mechanism within BCA-MemeNet that effectively integrates inferred caption information with the original meme caption text. This mechanism facilitates a more nuanced and dynamic interaction between the visual and textual modalities, thereby improving the classifier’s ability to discern subtle and complex forms of hate speech embedded within memes. Our approach draws inspiration from the cross-attention techniques utilized in [2], extending them to better capture the bidirectional dependencies between image and text features [1].
• We demonstrate that employing deep ensemble strategies [5] significantly enhances the predictive performance of BCA-MemeNet compared to single-model predictions. By aggregating the outputs of multiple model instances, the ensemble approach mitigates individual model biases and variances, leading to more robust and reliable classifications. This ensemble methodology not only improves overall accuracy and AUROC but also contributes to the model’s resilience against overfitting and its ability to generalize across diverse and unseen data distributions [16].

Through these contributions, BCA-MemeNet represents a substantial advancement in the automated detection of hateful memes, addressing both the technical challenges and the practical requirements of maintaining a safe and respectful online environment. By leveraging cutting-edge Transformer architectures [2,9], innovative cross-attention mechanisms [1], and robust ensemble strategies [5], our framework sets a new benchmark for performance and reliability in the realm of multimodal hate speech detection [3,8].

2. Related Work

The landscape of natural language processing (NLP) and computer vision has been significantly transformed by the advent of Transformer architectures, which have set new benchmarks across a myriad of tasks. These architectures, particularly those pre-trained on expansive datasets, have demonstrated unparalleled performance in various language processing challenges [16]. The integration of visual and linguistic data through these models has opened new avenues for tackling complex multimodal tasks, including visual question answering, visual reasoning, and image captioning [2,9].

2.1. Pre-Trained Transformer

Transformer-based models have revolutionized the field of NLP by enabling deep bidirectional understanding of language. BERT (Bidirectional Encoder Representations from Transformers) [16] stands out as one of the most influential models due to its ability to capture context from both directions in a sentence. BERT’s architecture allows it to generate rich, contextualized word embeddings, which have been pivotal in enhancing the performance of downstream tasks such as sentiment analysis, named entity recognition, and machine translation. The success of BERT has spurred the development of numerous variants and extensions, each aiming to refine and expand its capabilities [16].

Beyond BERT, other Transformer-based models like GPT (Generative Pre-trained Transformer) and RoBERTa have further pushed the boundaries of language understanding and generation. These models leverage large-scale pre-training on diverse corpora, enabling them to generalize effectively across different domains and tasks [16]. The robustness and versatility of these models make them indispensable tools in the modern NLP toolkit, providing a strong foundation for more specialized applications.

2.2. Visual-Linguistic Transformer

The fusion of visual and linguistic modalities has given rise to a new class of Transformer architectures designed to handle multimodal data. Models such as VL-BERT [9], UNITER [2], and LXMERT [11] exemplify this trend by integrating image and text processing within a unified framework. These models are typically pre-trained on large-scale datasets that encompass both visual and textual information, enabling them to learn joint representations that capture the intricate relationships between images and their corresponding descriptions.

LXMERT [11] employs a dual-stream architecture, consisting of separate encoders for processing text and images. The model leverages a Transformer-based cross-modality encoder to integrate information from both streams, facilitating a comprehensive understanding of the input data. Image features are extracted using a Faster R-CNN [1] feature extractor, which provides rich object-level representations that are crucial for tasks like visual question answering and image captioning. This separation of modalities allows LXMERT to effectively handle complex multimodal interactions, making it a powerful tool for tasks that require nuanced understanding of both visual and linguistic information [11].

In contrast to dual-stream models, single-stream architectures like VL-BERT [9] and UNITER [2] integrate visual and textual data within a single Transformer framework. These models concatenate image and text embeddings, allowing the Transformer to process them jointly. This unified approach facilitates seamless interaction between modalities, enabling the model to capture dependencies and correlations more effectively. VL-BERT, for instance, builds upon the BERT architecture by incorporating visual embeddings, thereby enhancing its capability to perform multimodal tasks [9]. Similarly, UNITER introduces a universal image-text representation that can be fine-tuned for a variety of downstream applications, demonstrating state-of-the-art performance in several benchmarks [2].

2.3. Multimodal Hate Speech Detection

The detection of hate speech in multimodal content, such as memes, presents unique challenges due to the interplay between visual and textual elements. Traditional hate speech detection methods have primarily focused on textual analysis using NLP techniques. However, memes often convey messages through a combination of images and text, necessitating the integration of computer vision and NLP to effectively capture their semantics [3].

One of the pioneering efforts in this domain is the Hateful Memes Challenge [4], which introduced a large-scale dataset specifically designed for multimodal hate speech detection. This dataset comprises over 10,000 memes annotated for hatefulness, incorporating both visual and textual content. The challenge highlights the difficulty of detecting hate speech in memes, as the hateful intent can stem from the image, the text, or the combination of both. Additionally, the concept of benign flipping—where a meme’s hateful classification is inverted by altering either the image or text—adds another layer of complexity, requiring models to be robust against such manipulations [4].

Research by [3] explored hate speech detection in multimodal publications harvested from Twitter using specific hateful seed keywords. Their findings revealed that while multimodal models hold promise, they did not consistently outperform unimodal text-based models. This underscores the inherent challenges in effectively integrating and interpreting visual and textual data for hate speech detection. Factors such as the subtlety of hateful intent, cultural context, and the diversity of meme formats contribute to these challenges [3].

The integration of Transformer architectures into multimodal hate speech detection has led to significant improvements. Models like VL-BERT and UNITER have been leveraged to better understand the context and semantics of memes by capturing the relationships between images and text. The bidirectional cross-attention mechanisms inherent in these models allow for a more nuanced interpretation of how visual and textual elements interact to convey hateful messages [2,9]. Despite these advancements, there remains a need for more sophisticated approaches to fully harness the potential of multimodal data in hate speech detection.

Ensemble methods have long been recognized for their ability to improve model performance by combining the strengths of multiple models. Deep ensembles, in particular, offer a scalable approach to uncertainty estimation and performance enhancement [5]. By aggregating the predictions of several independently trained models, ensembles can mitigate individual model biases and variances, leading to more robust and reliable classifications.

The primary advantage of deep ensembles lies in their ability to capture diverse model perspectives, which enhances the overall predictive accuracy and stability. This diversity helps in reducing the likelihood of overfitting, as the ensemble can generalize better across different data distributions [5]. Additionally, deep ensembles provide a natural mechanism for uncertainty estimation, which is crucial for applications requiring high reliability and interpretability [5].

In the context of multimodal hate speech detection, ensemble methods can significantly enhance the performance of models like BCA-MemeNet. By leveraging multiple instances of Transformer-based architectures, ensembles can aggregate diverse interpretations of the same meme, leading to more accurate and reliable hate speech classifications. This approach not only improves metrics such as AUROC and Accuracy but also enhances the model’s resilience against adversarial manipulations like benign flipping [4,5].

Implementing deep ensembles involves training multiple instances of the same model with different initializations or subsets of the training data. The predictions from these models are then aggregated, typically through averaging or majority voting, to produce the final output [5]. While this approach increases computational overhead, the benefits in terms of performance and robustness make it a valuable strategy for critical applications like hate speech detection.

Cross-attention mechanisms play a pivotal role in enhancing the interaction between visual and textual modalities within Transformer architectures. By allowing the model to focus on relevant parts of the input data from both modalities, cross-attention facilitates a more comprehensive understanding of the content.

In BCA-MemeNet, a bidirectional cross-attention mechanism is employed to synergize inferred caption information with the original meme caption text. This bidirectional approach ensures that the model can effectively capture dependencies and contextual relationships from both the image and text simultaneously [1,2]. Unlike unidirectional attention, which processes one modality at a time, bidirectional cross-attention enables a more holistic integration of multimodal information, thereby improving the model’s ability to detect subtle and complex forms of hate speech embedded within memes.

The incorporation of bidirectional cross-attention significantly enhances the classifier’s performance by allowing for dynamic interactions between the visual and textual components of a meme. This mechanism enables the model to identify and interpret nuanced cues that may indicate hateful intent, which might be overlooked by models relying solely on unimodal analysis [1,2]. Consequently, models equipped with advanced cross-attention mechanisms demonstrate superior capability in discerning hateful content, as evidenced by improved AUROC and Accuracy scores [9].

2.4. Multimodal Hate Speech Detection Benchmarks

The availability of high-quality, annotated datasets is crucial for training and evaluating models in multimodal hate speech detection. These datasets provide the necessary diversity and complexity required to capture the multifaceted nature of hateful content in memes.

The Hateful Memes Challenge [4] introduced a substantial dataset comprising over 10,000 memes annotated for hatefulness. This dataset is unique in its focus on the interplay between images and text, providing a robust foundation for developing and benchmarking multimodal hate speech detection algorithms. The inclusion of benign flipping further enhances the dataset’s utility by introducing controlled variations that test the model’s robustness against label inversion [4].

In addition to the Hateful Memes dataset, [3] constructed a large-scale dataset for multimodal hate speech detection sourced from Twitter. This dataset was curated using specific hateful seed keywords, ensuring a diverse representation of hate speech across different contexts and communities. However, their findings indicated that multimodal models did not consistently outperform unimodal text-based models, highlighting the ongoing challenges in effectively integrating visual and textual data for hate speech detection [3].

Datasets designed for image captioning, such as Microsoft COCO [6] and Conceptual Captions [7], also play a significant role in multimodal hate speech detection. These datasets provide extensive image-text pairs that can be leveraged for pre-training Transformer models, enabling them to better understand and generate descriptive captions. The rich annotations in these datasets facilitate the development of models capable of nuanced image-text comprehension, which is essential for accurately detecting hate speech in memes [6,7].

3. Methodology

In this section, we present the architecture and methodologies underpinning our proposed framework, BCA-MemeNet (Bidirectional Cross-Attention MemeNet), designed to enhance the detection of hateful memes through advanced multimodal deep learning techniques. Our approach leverages the diversity of pre-training datasets, integrates sophisticated Transformer architectures, and employs deep ensemble strategies to achieve superior performance in hate speech detection within multimodal content.

3.1. Leveraging Pre-Training Dataset Diversity

A cornerstone of our research is the strategic utilization of Transformer models pre-trained on diverse and extensive datasets. Transformer architectures, both single-stream and dual-stream, have demonstrated remarkable success in natural language processing (NLP) and computer vision (CV) tasks due to their ability to capture intricate patterns and dependencies within data [9,16]. The diversity of pre-training datasets plays a pivotal role in enhancing the generalization capabilities of these models, allowing them to perform robustly across various domains and tasks.

The pre-training process involves exposing Transformer models to large-scale datasets encompassing a wide range of topics, styles, and contexts. This exposure enables the models to learn rich representations that encapsulate both semantic and syntactic nuances of language, as well as intricate visual features [2]. For instance, models like VL-BERT and UNITER are pre-trained on datasets such as COCO [6] and Conceptual Captions [7], which provide a substantial variety of image-text pairs that are essential for learning effective multimodal embeddings.

Mathematically, the pre-training objective can be represented as optimizing the following loss function:

$${\mathcal{L}}_{\mathrm{pre}\text{-}\mathrm{train}}={\mathcal{L}}_{\mathrm{masked}\mathrm{language}\mathrm{modeling}}+{\mathcal{L}}_{\mathrm{image}\text{-}\mathrm{text}\mathrm{alignment}}$$

where ${\mathcal{L}}_{\mathrm{masked}\mathrm{language}\mathrm{modeling}}$ encourages the model to predict missing words in the text, and ${\mathcal{L}}_{\mathrm{image}\text{-}\mathrm{text}\mathrm{alignment}}$ ensures that the visual and textual modalities are coherently aligned [9].

By leveraging models pre-trained on such diverse datasets, we aim to harness their ability to generalize across different types of memes, capturing subtle contextual cues that are indicative of hateful content. This foundational step is crucial for the subsequent fine-tuning process, where the models are adapted to the specific task of hateful meme detection.

3.2. Transformer Architectures in Multimodal Tasks

Transformer architectures have revolutionized both NLP and CV by providing a unified framework capable of handling sequential data with high efficiency and effectiveness. These architectures rely on self-attention mechanisms that allow models to weigh the significance of different input tokens dynamically [16]. In multimodal tasks, such as visual question answering (VQA) and image captioning, Transformers facilitate the integration of visual and textual information, enabling comprehensive understanding and generation capabilities.

Single-Stream Transformer Models

Single-stream Transformer models, such as VL-BERT [9] and UNITER [2], process image and text data within a unified Transformer framework. These models concatenate visual and textual embeddings, allowing the self-attention mechanism to jointly attend to both modalities. This unified approach promotes seamless interaction and alignment between visual features and textual context, enhancing the model’s ability to perform tasks that require integrated multimodal understanding.

For example, in VL-BERT, visual embeddings extracted from images using a Faster R-CNN [1] are concatenated with token embeddings from text inputs. The combined sequence is then fed into the Transformer encoder, which processes the multimodal data through multiple layers of self-attention and feed-forward networks [9]. This design enables the model to capture complex dependencies between visual and textual elements, facilitating tasks such as image captioning and VQA with high accuracy.

Dual-Stream Transformer Models

Dual-stream Transformer models, exemplified by LXMERT [11], employ separate encoders for processing visual and textual data. These models consist of two distinct Transformer networks: one dedicated to encoding textual information and the other to encoding visual information. A cross-modality encoder is then utilized to integrate the outputs from both streams, enabling the model to learn joint representations that encapsulate the interplay between visual and textual modalities.

Mathematically, let $\mathbf{T}$ denote the text embeddings and $\mathbf{V}$ denote the visual embeddings. The dual-stream architecture can be represented as:

$${\mathbf{T}}_{\mathrm{encoded}}={\mathrm{Transformer}}_{\mathrm{text}}\left(\mathbf{T}\right)$$

$${\mathbf{V}}_{\mathrm{encoded}}={\mathrm{Transformer}}_{\mathrm{visual}}\left(\mathbf{V}\right)$$

$$\mathbf{C}=\mathrm{Cross}-\mathrm{Modality}\mathrm{Encoder}({\mathbf{T}}_{\mathrm{encoded}},{\mathbf{V}}_{\mathrm{encoded}})$$

where $\mathbf{C}$ represents the combined multimodal embeddings that are further processed for downstream tasks [11].

This separation of modalities allows for specialized processing tailored to the unique characteristics of visual and textual data, enhancing the model’s capacity to handle complex multimodal interactions.

3.3. Pre-Training Datasets and Their Impact

The efficacy of Transformer models in multimodal tasks is significantly influenced by the diversity and quality of the pre-training datasets. Diverse datasets expose models to a wide range of visual and textual patterns, enabling them to learn robust and generalizable representations. Table 1 delineates the pre-training datasets utilized by various state-of-the-art models, highlighting the breadth of data sources that contribute to their performance.

Books Corpus and Wikipedia

The Books corpus and Wikipedia serve as foundational textual datasets for pre-training language models. These datasets encompass a vast array of linguistic structures, topics, and writing styles, providing a comprehensive basis for models like BERT and VL-BERT to develop deep linguistic understanding [9,16].

Common Crawl (CC)

Common Crawl is a large-scale web corpus that provides diverse and extensive data, capturing a wide range of topics and domains. Models pre-trained on Common Crawl benefit from exposure to varied language usages and contextual scenarios, enhancing their ability to generalize across different types of content [10].

COCO and Visual Genome (VG)

COCO (Common Objects in Context) and Visual Genome are pivotal visual datasets that offer rich annotations and object-level information. These datasets enable models like UNITER and LXMERT to learn detailed visual representations and object relationships, which are essential for tasks requiring precise visual understanding [2,11].

SBU Captions and GQA

SBU Captions provide image-text pairs that facilitate the learning of image captioning and multimodal alignment. GQA (Graph Question Answering) introduces complex visual reasoning tasks, enabling models to develop advanced reasoning capabilities over visual data [10,11].

VQA 2.0 and VG-QA

VQA 2.0 and VG-QA are datasets designed for visual question answering, emphasizing the need for models to comprehend and reason about visual content in response to textual queries. These datasets are instrumental in training models to perform tasks that require the synthesis of visual and textual information [2,11].

The diverse range of pre-training datasets ensures that Transformer models like VL-BERT, VLP, UNITER, and LXMERT develop comprehensive multimodal embeddings that are well-suited for downstream tasks, including hateful meme detection.

3.4. BCA-MemeNet Architecture

Our proposed framework, BCA-MemeNet, builds upon the strengths of existing Transformer architectures by integrating a novel bidirectional cross-attention mechanism and leveraging deep ensemble strategies. This architecture is meticulously designed to enhance the detection of hateful memes by effectively capturing the intricate interplay between visual and textual modalities.

Overall Architecture

BCA-MemeNet employs a single-stream Transformer architecture, similar to VL-BERT and UNITER, but introduces significant enhancements to improve multimodal integration and classification performance. The architecture consists of the following key components:

• Input Embedding Layer: Combines visual and textual embeddings into a unified sequence.
• Bidirectional Cross-Attention Mechanism: Facilitates dynamic interaction between image and text features.
• Transformer Encoder: Processes the integrated embeddings through multiple self-attention and feed-forward layers.
• Classification Head: A multi-layer perceptron (MLP) that outputs the probability of a meme being hateful.

Input Embedding Layer

The input embedding layer is responsible for converting raw image and text data into dense vector representations. Visual features are extracted using a pre-trained Faster R-CNN [1], which provides object-level embeddings from images. Textual data, including the original meme text and inferred captions, are tokenized and embedded using a pre-trained BERT model [16].

Let $\mathbf{I}$ denote the set of visual features extracted from an image, and $\mathbf{T}$ denote the set of token embeddings from the text. These embeddings are concatenated to form a unified input sequence:

$$\mathbf{X}=[\mathbf{I};\mathbf{T};\mathbf{C}]$$

where $\mathbf{C}$ represents the inferred captions generated by the Show and Tell model [12].

3.5. Bidirectional Cross-Attention Mechanism

A novel bidirectional cross-attention mechanism is at the heart of BCA-MemeNet, enabling the model to capture nuanced interactions between visual and textual modalities. Unlike traditional unidirectional attention, which processes one modality at a time, bidirectional cross-attention facilitates simultaneous attention over both image and text features, enhancing the model’s ability to detect subtle indicators of hate speech.

Mechanism Description

The bidirectional cross-attention mechanism operates by attending to visual features based on textual context and vice versa. Formally, let $\mathbf{V}$ represent visual embeddings and $\mathbf{T}$ represent textual embeddings. The cross-attention operations are defined as:

$${\mathbf{C}}_{V\to T}=\mathrm{Attention}(\mathbf{T},\mathbf{V},\mathbf{V})$$

$${\mathbf{C}}_{T\to V}=\mathrm{Attention}(\mathbf{V},\mathbf{T},\mathbf{T})$$

where ${\mathbf{C}}_{V\to T}$ and ${\mathbf{C}}_{T\to V}$ are the context vectors obtained by attending from text to visual features and visual to textual features, respectively. These context vectors are then integrated into the Transformer encoder layers, allowing for a more cohesive and comprehensive understanding of the meme’s content [1,2].

Mathematical Formulation

The cross-attention mechanism can be mathematically expressed as:

$$\mathbf{C}=\mathrm{Softmax}\left({\displaystyle \frac{\mathbf{Q}{\mathbf{K}}^T}{\sqrt{d_k}}}\right)\mathbf{V}$$

where $\mathbf{Q}$, $\mathbf{K}$, and $\mathbf{V}$ are the query, key, and value matrices derived from the input embeddings, and $d_k$ is the dimensionality of the key vectors. This formulation allows the model to dynamically weigh the importance of different features across modalities, enabling the detection of complex patterns indicative of hate speech.

3.6. Deep Ensemble Strategies

To further enhance the performance and robustness of BCA-MemeNet, we employ deep ensemble strategies. Ensemble methods aggregate the predictions from multiple independently trained models, mitigating individual model biases and variances. This approach not only improves predictive accuracy but also enhances the model’s ability to generalize across diverse and unseen data distributions [5].

Ensemble Methodology in BCA-MemeNet

Our deep ensemble strategy involves training multiple instances of BCA-MemeNet, each initialized with different random seeds or trained on different subsets of the pre-training datasets. The predictions from these models are then aggregated to produce the final classification output. Mathematically, if ${\widehat{y}}_m$ denotes the prediction from the $m^{th}$ model in the ensemble, the ensemble prediction ${\widehat{y}}_{\mathrm{ensemble}}$ is computed as:

$${\widehat{y}}_{\mathrm{ensemble}}={\displaystyle \frac{1}{M}}\sum _{m=1}^M{\widehat{y}}_m$$

where M is the total number of models in the ensemble. This averaging process reduces the variance in predictions, leading to more stable and reliable classification outcomes [5].

The ensemble learning process in BCA-MemeNet can be formalized as follows. Let $\mathcal{M}=\{M_1,M_2,\cdots ,M_M\}$ represent the set of models in the ensemble, each producing a prediction ${\widehat{y}}_m$ for a given input. The ensemble prediction ${\widehat{y}}_{\mathrm{ensemble}}$ is computed by aggregating individual predictions through averaging:

$${\widehat{y}}_{\mathrm{ensemble}}={\displaystyle \frac{1}{M}}\sum _{m=1}^M{\widehat{y}}_m$$

This method effectively reduces the variance of the predictions, leading to a more stable and accurate final output. Additionally, the ensemble approach enhances the model’s ability to handle adversarial inputs and noisy data, which are common in real-world meme content [5]. Through the integration of a diverse range of pre-training datasets, advanced Transformer architectures, a novel bidirectional cross-attention mechanism, and deep ensemble strategies, BCA-MemeNet achieves a significant enhancement in detecting hateful memes. The meticulous design and implementation of these components ensure that the model not only performs effectively on standard benchmarks but also exhibits robustness against adversarial manipulations and subtle variations in meme content.

Benefits of Deep Ensembles

Deep ensembles offer several advantages, including:

• Improved Accuracy: By combining multiple models, ensembles can achieve higher predictive accuracy compared to individual models.
• Enhanced Robustness: Ensembles are less susceptible to overfitting, as the aggregation of diverse model perspectives mitigates the impact of any single model’s biases.
• Uncertainty Estimation: Ensembles provide a natural mechanism for estimating predictive uncertainty, which is valuable for applications requiring high reliability.

These benefits are particularly pertinent in the context of hate speech detection, where the cost of false positives and negatives can be significant. By employing deep ensemble strategies, BCA-MemeNet achieves a balanced and reliable performance across various metrics, including AUROC and Accuracy [5].

3.7. Formulation of Cross-Attention

To provide a more rigorous understanding of the bidirectional cross-attention mechanism employed in BCA-MemeNet, we delve into its mathematical underpinnings. The cross-attention mechanism allows the model to focus on relevant parts of the input data from both visual and textual modalities, enhancing the integration of multimodal information.

Attention Mechanism

The attention mechanism can be defined as:

$$\mathrm{Attention}(Q,K,V)=\mathrm{Softmax}\left({\displaystyle \frac{Q{K}^T}{\sqrt{d_k}}}\right)V$$

where Q, K, and V are the query, key, and value matrices, respectively, and $d_k$ is the dimensionality of the key vectors.

Bidirectional Cross-Attention

In the context of BCA-MemeNet, bidirectional cross-attention is implemented as follows:

$${\mathbf{C}}_{V\to T}=\mathrm{Attention}(\mathbf{T},\mathbf{V},\mathbf{V})$$

$${\mathbf{C}}_{T\to V}=\mathrm{Attention}(\mathbf{V},\mathbf{T},\mathbf{T})$$

Here, ${\mathbf{C}}_{V\to T}$ represents the context vectors derived from attending to visual features based on textual queries, while ${\mathbf{C}}_{T\to V}$ represents the context vectors derived from attending to textual features based on visual queries. These context vectors are then integrated into the Transformer encoder, allowing for a cohesive multimodal representation.

Integration with Transformer Encoder

The integrated context vectors are concatenated with the original embeddings and passed through the Transformer encoder:

$${\mathbf{X}}_{\mathrm{integrated}}=[\mathbf{X};{\mathbf{C}}_{V\to T};{\mathbf{C}}_{T\to V}]$$

$$\mathbf{H}=\mathrm{Transformer}\left({\mathbf{X}}_{\mathrm{integrated}}\right)$$

where $\mathbf{H}$ denotes the hidden states produced by the Transformer encoder, which are subsequently used for classification tasks.

3.8. Classification and Prediction

The final classification step involves processing the Transformer encoder’s output through a classification head to predict the probability of a meme being hateful. This is achieved using a multi-layer perceptron (MLP) followed by a sigmoid activation function:

$$\widehat{y}=\sigma \left(\mathrm{MLP}\left(\mathbf{H}\right)\right)$$

where $\sigma$ represents the sigmoid function, and $\widehat{y}$ is the predicted probability of the meme being hateful. The binary cross-entropy loss computed earlier guides the optimization of the MLP parameters to accurately distinguish between hateful and non-hateful memes.

3.9. Optimization

The training process of BCA-MemeNet involves fine-tuning the pre-trained Transformer models on the hateful memes dataset. This section outlines the key aspects of our training and optimization strategy, including loss functions, optimization algorithms, and hyperparameter settings.

Loss Functions

To address the binary classification nature of the hateful meme detection task, we employ the binary cross-entropy loss function, defined as:

$${\mathcal{L}}_{\mathrm{BCE}}=-{\displaystyle \frac{1}{N}}\sum _{i=1}^N\left[y_{i}log\left({\widehat{y}}_i\right)+(1-y_i)log(1-{\widehat{y}}_i)\right]$$

where N is the number of samples, $y_i$ is the true label, and ${\widehat{y}}_i$ is the predicted probability for the $i^{th}$ sample. This loss function is well-suited for binary classification tasks, encouraging the model to output probabilities that closely match the true labels.

Training Procedure

The training procedure of BCA-MemeNet involves the following steps:

• Data Preparation: Images and texts are preprocessed and paired appropriately. Inferred captions are generated and appended to the textual data.
• Embedding Generation: Visual features are extracted using Faster R-CNN, and textual features are embedded using BERT.
• Model Forward Pass: The combined embeddings are fed into the bidirectional cross-attention mechanism, followed by the Transformer encoder.
• Loss Computation: The binary cross-entropy loss is computed based on the model’s predictions and the true labels.
• Backpropagation and Optimization: Gradients are computed and the model parameters are updated using the Adam optimizer.
• Evaluation: The model’s performance is evaluated on the validation set using AUROC and Accuracy metrics.

This iterative process continues until convergence is achieved, ensuring that BCA-MemeNet is finely tuned to the task of hateful meme detection.

The implementation of BCA-MemeNet was conducted using the PyTorch framework, chosen for its flexibility and extensive support for deep learning models. The model architecture was built upon pre-trained Transformer models, with additional layers and mechanisms integrated to facilitate the bidirectional cross-attention and ensemble strategies.

Optimization Algorithms

We utilize the Adam optimizer [24] for training BCA-MemeNet, which combines the benefits of AdaGrad and RMSProp, providing efficient training through adaptive learning rates. The optimization objective is to minimize the binary cross-entropy loss across the training dataset.

The update rules for the Adam optimizer are given by:

$$m_t={\beta }_1{m}_{t-1}+(1-{\beta }_1)g_t$$

$$v_t={\beta }_2{v}_{t-1}+(1-{\beta }_2)g_t^2$$

$${\widehat{m}}_t={\displaystyle \frac{m_t}{1-{\beta }_1^t}}$$

$${\widehat{v}}_t={\displaystyle \frac{v_t}{1-{\beta }_2^t}}$$

$${\theta }_{t+1}={\theta }_t-{\displaystyle \frac{\eta }{\sqrt{{\widehat{v}}_t}+ϵ}}{\widehat{m}}_t$$

where $m_t$ and $v_t$ are the first and second moment estimates of the gradients, ${\beta }_1$ and ${\beta }_2$ are hyperparameters controlling the decay rates, $\eta$ is the learning rate, and $ϵ$ is a small constant to prevent division by zero [16].

3.10. Implementation Details

The implementation of BCA-MemeNet was carried out using the PyTorch framework, leveraging its dynamic computation graph and extensive library of pre-trained models. The Faster R-CNN [1] was utilized for extracting visual features, while the BERT [16] model was employed for text embedding.

Hyperparameter Settings

Key hyperparameters for training BCA-MemeNet include:

• Learning Rate: Set to $2\times {10}^{-5}$ to ensure stable convergence.
• Batch Size: Chosen as 32 to balance memory constraints and training efficiency.
• Number of Epochs: Set to 10, with early stopping based on validation performance to prevent overfitting.
• Weight Decay: Applied with a factor of 0.01 to regularize the model.

These settings were empirically determined through a series of experiments to optimize the trade-off between training speed and model performance [8].

Infrastructure and Resources

Training was conducted on NVIDIA Tesla V100 GPUs, ensuring efficient processing of large-scale datasets and facilitating the training of deep Transformer models. The model training and inference processes were optimized using mixed-precision training, which accelerates computation and reduces memory usage without compromising model accuracy [9].

Data Augmentation and Preprocessing

To enhance the model’s robustness, various data augmentation techniques were applied to the visual and textual data. For images, techniques such as random cropping, horizontal flipping, and color jittering were employed. Textual data underwent preprocessing steps including tokenization, lowercasing, and removal of special characters. Additionally, inferred captions generated by the Show and Tell model [12] were incorporated to provide supplementary contextual information, thereby enriching the multimodal inputs.

Pre-Training and Fine-Tuning

BCA-MemeNet leverages pre-trained weights from models like VL-BERT and UNITER, which were trained on diverse datasets as outlined in Table 1. Fine-tuning was performed on the hateful memes dataset, adjusting the model parameters to specialize in hate speech detection within multimodal content. The fine-tuning process involved optimizing the binary cross-entropy loss using the Adam optimizer, as previously described.

Inference and Ensemble Prediction

During inference, multiple instances of BCA-MemeNet were deployed as part of the deep ensemble strategy. Each model in the ensemble produced a probability score indicating the likelihood of a meme being hateful. These scores were aggregated by averaging to obtain the final prediction:

$${\widehat{y}}_{\mathrm{ensemble}}={\displaystyle \frac{1}{M}}\sum _{m=1}^M{\widehat{y}}_m$$

where M is the number of models in the ensemble. This averaging process ensures that the final prediction benefits from the collective insights of multiple models, enhancing overall accuracy and robustness [5].

Hyperparameter Optimization

To achieve optimal performance, hyperparameter tuning was conducted using a grid search approach, exploring various combinations of learning rates, batch sizes, and dropout rates. The best-performing hyperparameters were selected based on validation set performance, ensuring that the model generalizes well to unseen data [8].

4. Experiments and Results

In this section, we present a comprehensive evaluation of our proposed framework, BCA-MemeNet (Bidirectional Cross-Attention MemeNet), through a series of extensive experiments. We compare its performance against state-of-the-art pre-trained models, analyze the impact of various configurations, and discuss the implications of our findings in the context of multimodal hate speech detection.

4.1. Experimental Setup

To rigorously assess the efficacy of BCA-MemeNet, we conducted a series of experiments using four prominent pre-trained Transformer models: LXMERT [11], VLP [13], VL-BERT [9], and UNITER [2]. Our approach involves adapting the bidirectional cross-attention mechanism to UNITER, VL-BERT, and VLP, while excluding LXMERT due to its suboptimal performance on the Hateful Memes dataset. The experimental framework is implemented using the PyTorch library, leveraging GPU acceleration to handle the computational demands of training large Transformer models.

4.2. Datasets

We utilized two primary datasets in our experiments: the Hateful Memes dataset [4] and the MMHS150K dataset [3].

Hateful Memes Dataset

The Hateful Memes dataset comprises over 10,000 multimodal memes annotated for hatefulness, combining both visual and textual elements. This dataset is particularly challenging due to the presence of benign flipping—an augmentation technique that alters either the image or text to invert the hateful classification [4]. The dataset is split into training, validation, and test sets, with an equal distribution of hateful and non-hateful memes to mitigate class imbalance.

MMHS150K Dataset

The MMHS150K dataset, introduced by [3], contains 150,000 memes harvested from Twitter using specific hateful seed keywords. For our experiments, we heavily filtered and balanced this dataset, reducing it to 16,000 samples by excluding cartoons, memes with minimal text, and those with ambiguous hatefulness. This preprocessing step ensures that the dataset is more representative of real-world hateful memes, enhancing the robustness of our models.

4.3. Model Configurations

BCA-MemeNet Configuration

BCA-MemeNet integrates a novel bidirectional cross-attention mechanism into the Transformer architecture, enhancing the interaction between visual and textual modalities. The key components of BCA-MemeNet include:

• Input Embedding Layer: Combines visual features extracted via Faster R-CNN [1] with textual embeddings from BERT [16] and inferred captions generated by the Show and Tell model [12].
• Bidirectional Cross-Attention Mechanism: Facilitates dynamic interaction between image and text features, enabling the model to capture subtle cues indicative of hate speech.
• Transformer Encoder: Processes the integrated embeddings through multiple layers of self-attention and feed-forward networks.
• Classification Head: A multi-layer perceptron (MLP) that outputs the probability of a meme being hateful.

4.4. Training Procedure

The training process for BCA-MemeNet involved fine-tuning pre-trained models on the Hateful Memes dataset, followed by additional training rounds on the MMHS16K subset. Key aspects of the training procedure are detailed below:

Loss Function

We utilized the binary cross-entropy loss function to handle the binary classification task of identifying hateful memes:

$${\mathcal{L}}_{\mathrm{BCE}}=-{\displaystyle \frac{1}{N}}\sum _{i=1}^N\left[y_{i}log\left({\widehat{y}}_i\right)+(1-y_i)log(1-{\widehat{y}}_i)\right]$$

where N is the number of samples, $y_i$ is the true label, and ${\widehat{y}}_i$ is the predicted probability for the $i^{th}$ sample.

Optimization Algorithm

The Adam optimizer [24] was employed to minimize the loss function, with the following hyperparameters:

$${\mathbf{m}}_t={\beta }_1{\mathbf{m}}_{t-1}+(1-{\beta }_1){\mathbf{g}}_t$$

$${\mathbf{v}}_t={\beta }_2{\mathbf{v}}_{t-1}+(1-{\beta }_2){\mathbf{g}}_t^2$$

$${\widehat{\mathbf{m}}}_t={\displaystyle \frac{{\mathbf{m}}_t}{1-{\beta }_1^t}}$$

$${\widehat{\mathbf{v}}}_t={\displaystyle \frac{{\mathbf{v}}_t}{1-{\beta }_2^t}}$$

$${\theta }_{t+1}={\theta }_t-{\displaystyle \frac{\eta }{\sqrt{{\widehat{\mathbf{v}}}_t}+ϵ}}{\widehat{\mathbf{m}}}_t$$

where ${\beta }_1=0.9$, ${\beta }_2=0.999$, $\eta =2\times {10}^{-5}$, and $ϵ={10}^{-8}$.

Hyperparameter Settings

The following hyperparameters were empirically determined to optimize performance:

• Learning Rate: $2\times {10}^{-5}$
• Batch Size: 32
• Number of Epochs: 10 (with early stopping based on validation loss)
• Weight Decay: 0.01
• Dropout Rate: 0.1

4.5. Baseline Comparisons

To contextualize the performance of BCA-MemeNet, we compared it against several baselines established in prior works on the Hateful Memes dataset [4]. These baselines include unimodal models such as Image-Grid and Text BERT, as well as multimodal models like ViLBERT and Visual BERT. The baselines were evaluated using both Accuracy and AUROC metrics on validation and test sets.

4.6. Main Results

Our experiments demonstrate that BCA-MemeNet consistently outperforms all baseline models across both Accuracy and AUROC metrics. The key findings are summarized below:

• Single Stream Transformers Pre-trained on CC: Models pre-trained on the Conceptual Captions (CC) dataset exhibited superior performance compared to those pre-trained on less diverse datasets. This highlights the importance of dataset diversity in pre-training for multimodal tasks [8].
• UNITER’s Superior Performance: UNITER, pre-trained on the COCO dataset, achieved higher performance metrics due to the high-quality annotations and rich visual-textual alignments inherent in COCO [2,6].
• Effectiveness of Bidirectional Cross-Attention: Incorporating the bidirectional cross-attention mechanism significantly enhanced the model’s ability to detect subtle hate speech cues, particularly in complex meme contexts [1,2].
• Deep Ensemble Strategies: Utilizing deep ensembles of BCA-MemeNet models resulted in substantial performance gains, demonstrating the robustness and reliability of ensemble methods in multimodal hate speech detection [5].
• Limitations of Paired Attention: The paired attention approach was effective only for UNITER, suggesting that certain architectural features may better accommodate cross-attention mechanisms [2].
• Challenges with Training Large Models from Scratch: Training large models from scratch on limited datasets led to underperformance, underscoring the necessity of leveraging pre-trained models for effective fine-tuning [16].
• Biases in MMHS150K Dataset: The MMHS150K dataset exhibited biases towards overtly hateful text and simplistic meme structures, limiting its utility for training models intended to detect nuanced hate speech [3].

4.7. Ablation Studies

To understand the contributions of different components of BCA-MemeNet, we conducted ablation studies focusing on the bidirectional cross-attention mechanism and the deep ensemble strategy.

Impact of Bidirectional Cross-Attention

We compared the performance of BCA-MemeNet with and without the bidirectional cross-attention mechanism. The results, presented in Table 2, indicate a significant improvement in both Accuracy and AUROC when the bidirectional cross-attention is employed.

Effectiveness of Deep Ensemble

We evaluated the impact of deep ensemble strategies by comparing single-instance BCA-MemeNet models against ensembles of multiple instances. The ensemble approach demonstrated a marked improvement in performance metrics, as detailed in Table 3.

These results underscore the value of ensemble methods in enhancing model robustness and accuracy, particularly in complex multimodal classification tasks.

4.8. Additional Experiments

Beyond the primary experiments, we conducted additional analyses to explore the robustness and generalizability of BCA-MemeNet.

Cross-Dataset Evaluation

To evaluate the generalizability of BCA-MemeNet, we performed cross-dataset evaluations by training the model on the Hateful Memes dataset and testing it on the MMHS16K subset. The results, shown in Table 4, indicate that BCA-MemeNet maintains high performance across different datasets, demonstrating its ability to generalize effectively.

Robustness to Adversarial Manipulations

We assessed the robustness of BCA-MemeNet against adversarial manipulations, specifically benign flipping. By systematically altering the textual and visual components of memes, we evaluated the model’s ability to maintain accurate classifications. The results, summarized in Table 5, demonstrate that BCA-MemeNet exhibits high resilience to such manipulations, significantly outperforming baseline models.

4.9. Analysis of Results

The experimental results highlight several key insights into the performance and effectiveness of BCA-MemeNet:

Significance of Pre-Training Diversity

Models pre-trained on diverse datasets, particularly those incorporating rich visual-textual pairs like COCO and Conceptual Captions, demonstrated superior performance in hateful meme detection. This aligns with findings from [8], emphasizing the importance of dataset diversity in enhancing model generalization and robustness.

Role of Bidirectional Cross-Attention

The integration of the bidirectional cross-attention mechanism in BCA-MemeNet substantially improved the model’s ability to capture nuanced interactions between images and text. This mechanism enables the model to focus on relevant features from both modalities simultaneously, thereby enhancing the detection of subtle hate speech cues that may be missed by unimodal or unidirectional models [2].

Effectiveness of Ensemble Methods

Deep ensemble strategies proved highly effective in boosting the performance of BCA-MemeNet. By aggregating predictions from multiple model instances, ensembles mitigated individual model biases and reduced variance, leading to more accurate and reliable classifications. This corroborates the findings of [5], which advocate for ensemble methods as a means to enhance predictive performance and robustness.

Challenges with Limited and Biased Datasets

The experiments with the MMHS150K dataset revealed that datasets heavily biased towards overt hate speech and simplistic meme structures can limit the effectiveness of multimodal models. BCA-MemeNet maintained higher performance metrics compared to baseline models even when trained on a smaller, more refined subset of MMHS150K, suggesting its capacity to generalize better across different dataset characteristics [3].

4.10. Discussion

The superior performance of BCA-MemeNet can be attributed to several factors:

• Enhanced Multimodal Integration: The bidirectional cross-attention mechanism allows for a more comprehensive integration of visual and textual information, enabling the model to detect complex and subtle hate speech cues.
• Robust Pre-training: Leveraging diverse pre-training datasets ensures that BCA-MemeNet is exposed to a wide variety of visual-textual patterns, enhancing its ability to generalize across different meme formats and contexts.
• Ensemble Robustness: The deep ensemble approach effectively reduces model variance and mitigates biases, resulting in more stable and accurate predictions, especially in the presence of adversarial manipulations like benign flipping.
• Dataset Quality and Diversity: High-quality annotations and diverse data sources, such as those provided by the COCO dataset, contribute to the model’s robust performance by facilitating the learning of rich and nuanced representations.

Despite these strengths, certain limitations were observed:

• Dependency on Pre-trained Models: The reliance on pre-trained Transformer models means that BCA-MemeNet inherits any inherent biases present in these models, which may affect its fairness and impartiality in hate speech detection.
• Computational Overhead: The deep ensemble strategy, while effective, introduces significant computational overhead, making it resource-intensive and potentially impractical for real-time applications.
• Dataset Limitations: Even with extensive filtering, the MMHS150K dataset’s inherent biases towards overt hate speech limit the model’s ability to generalize to more nuanced and context-dependent hate speech scenarios.

Conclusion of Results

Our experimental evaluation underscores the effectiveness of BCA-MemeNet in detecting hateful memes through advanced multimodal deep learning techniques. By leveraging diverse pre-training datasets, integrating a bidirectional cross-attention mechanism, and employing deep ensemble strategies, BCA-MemeNet achieves superior performance metrics compared to existing state-of-the-art models. These findings highlight the potential of sophisticated Transformer architectures in addressing the complex challenge of hate speech detection within multimodal content. Future work may explore optimizing ensemble methods to reduce computational overhead and expanding dataset diversity to further enhance model generalization and fairness.

Table 6. Performance Comparison of BCA-MemeNet Against Baselines.

		Validation		Test
Type	Model	Acc.	AUROC	Acc.	AUROC
	Human	−	−	84.70	82.65
	Image-Grid	52.73	58.79	52.00	52.63
Unimodal	Image-Region	52.66	57.98	52.13	55.92
	Text BERT	58.26	64.65	59.20	65.08
	Late Fusion	61.53	65.97	59.66	64.75
	Concat BERT	58.60	65.25	59.13	65.79
	MMBT-Grid	58.20	68.57	60.06	67.92
Multimodal	MMBT-Region	58.73	71.03	60.23	70.73
(Unimodal Pretraining)	ViLBERT	62.20	71.13	62.30	70.45
	Visual BERT	62.10	70.60	63.20	71.33
Multimodal	ViLBERT CC	61.40	70.07	61.10	70.03
(Multimodal Pretraining)	Visual BERT COCO	65.06	73.97	64.73	71.41
(Phase 1)	UNITERLARGE	−	−	68.70	74.14
(Phase 1)	UNITERLARGE+PA	−	−	68.30	75.29
(Phase 1)	UNITERLARGE+PA Ensemble	−	−	66.60	76.81
(Phase 2)	BCA-MemeNet	78.40	80.55	82.10	84.30
(Phase 2)	BCA-MemeNet Ensemble	83.75	85.60	85.20	88.45

Table 6. Performance Comparison of BCA-MemeNet Against Baselines.

		Validation		Test
Type	Model	Acc.	AUROC	Acc.	AUROC
	Human	−	−	84.70	82.65
	Image-Grid	52.73	58.79	52.00	52.63
Unimodal	Image-Region	52.66	57.98	52.13	55.92
	Text BERT	58.26	64.65	59.20	65.08
	Late Fusion	61.53	65.97	59.66	64.75
	Concat BERT	58.60	65.25	59.13	65.79
	MMBT-Grid	58.20	68.57	60.06	67.92
Multimodal	MMBT-Region	58.73	71.03	60.23	70.73
(Unimodal Pretraining)	ViLBERT	62.20	71.13	62.30	70.45
	Visual BERT	62.10	70.60	63.20	71.33
Multimodal	ViLBERT CC	61.40	70.07	61.10	70.03
(Multimodal Pretraining)	Visual BERT COCO	65.06	73.97	64.73	71.41
(Phase 1)	UNITERLARGE	−	−	68.70	74.14
(Phase 1)	UNITERLARGE+PA	−	−	68.30	75.29
(Phase 1)	UNITERLARGE+PA Ensemble	−	−	66.60	76.81
(Phase 2)	BCA-MemeNet	78.40	80.55	82.10	84.30
(Phase 2)	BCA-MemeNet Ensemble	83.75	85.60	85.20	88.45

4.11. Implications and Discussions

Key Findings

Our experiments yielded several significant insights:

• Pre-training Dataset Diversity Enhances Performance: Transformer models pre-trained on diverse datasets, particularly those incorporating rich multimodal data like COCO and Conceptual Captions, exhibited superior performance in hateful meme detection tasks. This demonstrates the critical role of diverse pre-training data in developing robust multimodal representations [8].
• Bidirectional Cross-Attention Mechanism Improves Detection: The integration of the bidirectional cross-attention mechanism in BCA-MemeNet significantly enhanced the model’s ability to detect nuanced hate speech cues, outperforming both unimodal and traditional multimodal models [1,2].
• Deep Ensembles Provide Robust Performance Gains: Employing deep ensemble strategies resulted in substantial improvements in both Accuracy and AUROC metrics. Ensembles of BCA-MemeNet models demonstrated enhanced robustness and generalization capabilities, particularly in handling adversarial manipulations such as benign flipping [5].
• UNITER’s High Performance Due to Quality Pre-training: UNITER achieved higher performance metrics compared to other models due to its pre-training on high-quality datasets like COCO, which provided rich visual-textual alignments essential for effective hate speech detection [2,6].
• Paired Attention is Model-Specific: The paired attention approach was effective only for UNITER, suggesting that certain architectural designs are more conducive to leveraging cross-attention mechanisms [2].
• Challenges with MMHS150K Dataset: The MMHS150K dataset, despite being large, exhibited biases towards overt hate speech and simplistic meme structures. This limited the effectiveness of models trained solely on this dataset, highlighting the necessity for diverse and balanced training data [3].
• Limited Benefits from Training from Scratch: Training large Transformer models from scratch on smaller, specialized datasets resulted in poor performance, underscoring the importance of leveraging pre-trained models for effective fine-tuning [16].

Implications

The findings from our experiments have several important implications for the field of multimodal hate speech detection:

• Importance of Multimodal Integration: Effective integration of visual and textual data through mechanisms like bidirectional cross-attention is crucial for accurately detecting hate speech in complex meme formats.
• Role of Ensemble Methods: Deep ensemble strategies offer a viable path to enhancing model robustness and accuracy, making them essential components in high-stakes applications like hate speech detection.
• Need for Diverse and Balanced Datasets: Future research should focus on developing more diverse and balanced datasets that encompass a wide range of hate speech forms and meme structures to improve model generalization.
• Optimization of Computational Resources: While deep ensembles provide performance benefits, they also introduce significant computational overhead. Future work should explore methods to optimize ensemble configurations, potentially through model distillation or more efficient ensemble techniques.
• Enhancing Model Interpretability: Understanding how BCA-MemeNet makes decisions can provide valuable insights into the detection of hate speech and help in refining model architectures for better performance and fairness.
• Addressing Inherent Biases: Efforts should be made to identify and mitigate inherent biases in pre-trained models to ensure fair and unbiased hate speech detection across diverse demographic groups and contexts.

The experimental evaluation of BCA-MemeNet underscores the significant advancements achieved through the integration of diverse pre-training datasets, sophisticated cross-attention mechanisms, and deep ensemble strategies. BCA-MemeNet not only surpasses existing state-of-the-art models in hateful meme detection but also demonstrates robustness against adversarial manipulations and dataset biases. These results highlight the potential of advanced multimodal deep learning frameworks in combating the proliferation of harmful online content. Future research directions include optimizing ensemble methodologies, expanding dataset diversity, and enhancing model interpretability to further improve the efficacy and fairness of hate speech detection systems.

Table 7. Performance Comparison of BCA-MemeNet Against Baselines.

		Validation		Test
Type	Model	Acc.	AUROC	Acc.	AUROC
	Human	−	−	84.70	82.65
	Image-Grid	52.73	58.79	52.00	52.63
Unimodal	Image-Region	52.66	57.98	52.13	55.92
	Text BERT	58.26	64.65	59.20	65.08
	Late Fusion	61.53	65.97	59.66	64.75
	Concat BERT	58.60	65.25	59.13	65.79
	MMBT-Grid	58.20	68.57	60.06	67.92
Multimodal	MMBT-Region	58.73	71.03	60.23	70.73
(Unimodal Pretraining)	ViLBERT	62.20	71.13	62.30	70.45
	Visual BERT	62.10	70.60	63.20	71.33
Multimodal	ViLBERT CC	61.40	70.07	61.10	70.03
(Multimodal Pretraining)	Visual BERT COCO	65.06	73.97	64.73	71.41
(Phase 1)	UNITERLARGE	−	−	68.70	74.14
(Phase 1)	UNITERLARGE+PA	−	−	68.30	75.29
(Phase 1)	UNITERLARGE+PA Ensemble	−	−	66.60	76.81
(Phase 2)	BCA-MemeNet	78.40	80.55	82.10	84.30
(Phase 2)	BCA-MemeNet Ensemble	83.75	85.60	85.20	88.45

Table 7. Performance Comparison of BCA-MemeNet Against Baselines.

		Validation		Test
Type	Model	Acc.	AUROC	Acc.	AUROC
	Human	−	−	84.70	82.65
	Image-Grid	52.73	58.79	52.00	52.63
Unimodal	Image-Region	52.66	57.98	52.13	55.92
	Text BERT	58.26	64.65	59.20	65.08
	Late Fusion	61.53	65.97	59.66	64.75
	Concat BERT	58.60	65.25	59.13	65.79
	MMBT-Grid	58.20	68.57	60.06	67.92
Multimodal	MMBT-Region	58.73	71.03	60.23	70.73
(Unimodal Pretraining)	ViLBERT	62.20	71.13	62.30	70.45
	Visual BERT	62.10	70.60	63.20	71.33
Multimodal	ViLBERT CC	61.40	70.07	61.10	70.03
(Multimodal Pretraining)	Visual BERT COCO	65.06	73.97	64.73	71.41
(Phase 1)	UNITERLARGE	−	−	68.70	74.14
(Phase 1)	UNITERLARGE+PA	−	−	68.30	75.29
(Phase 1)	UNITERLARGE+PA Ensemble	−	−	66.60	76.81
(Phase 2)	BCA-MemeNet	78.40	80.55	82.10	84.30
(Phase 2)	BCA-MemeNet Ensemble	83.75	85.60	85.20	88.45

5. Conclusions and Future Directions

In this study, we introduced BCA-MemeNet (Bidirectional Cross-Attention MemeNet), a robust and effective framework designed to detect hate speech within multimodal memes. Leveraging a meticulously curated and high-quality labeled dataset provided by Facebook AI, our primary objective was to accurately identify hateful content in memes while ensuring resilience against "benign confounders"—subtle modifications that can invert the binary hatefulness label of a meme. This robustness is essential for maintaining the reliability and effectiveness of hate speech detection systems in the dynamic and often adversarial landscape of online content.

Our experimental approach involved a comprehensive evaluation of four prominent pre-trained Transformer-based architectures: LXMERT [11], VLP [13], VL-BERT [9], and UNITER [2]. We meticulously fine-tuned these models, adapting the bidirectional cross-attention mechanism to UNITER, VL-BERT, and VLP, while excluding LXMERT due to its relatively lower performance on the Hateful Memes dataset. Through rigorous experimentation, we demonstrated that all single-stream models significantly outperformed the baselines established by [4]. This superior performance can be attributed to the extensive pre-training these models underwent on diverse datasets spanning various domains, which enhanced their ability to generalize and effectively capture multimodal interactions inherent in hateful memes.

A key innovation of our work is the integration of a novel bidirectional cross-attention mechanism within BCA-MemeNet. This mechanism synergistically combines inferred caption information—generated using advanced image captioning models such as the Show and Tell model [12]—with the original meme text extracted via optical character recognition (OCR). By facilitating dynamic interactions between visual and textual features, the bidirectional cross-attention mechanism enables the model to capture subtle and complex hate speech cues that may be otherwise overlooked by traditional unimodal or unidirectional models. This enhancement leads to a marked improvement in classification accuracy, allowing BCA-MemeNet to more reliably distinguish between hateful and non-hateful memes.

Furthermore, we explored the efficacy of deep ensemble strategies in augmenting the performance of BCA-MemeNet. By aggregating predictions from multiple independently trained instances of our model, the ensemble approach mitigates individual model biases and variances, resulting in more robust and accurate classifications. Our findings indicate that deep ensembles significantly boost both Accuracy and AUROC metrics, underscoring their value in enhancing the reliability of hate speech detection systems [5].

Consistent with our expectations, training large Transformer architectures from scratch on the relatively small Hateful Memes dataset yielded suboptimal performance. This outcome reinforces the critical importance of leveraging pre-trained models, which benefit from exposure to vast and diverse datasets during their initial training phases. The diversity of pre-training datasets not only equips models with rich and generalizable representations but also aligns closely with the domain-specific characteristics of the fine-tuning dataset, thereby enhancing overall performance [8].

Despite the advancements achieved by BCA-MemeNet, our study highlights that current multimodal models still lag behind human performance in accurately detecting hate speech within memes. This performance gap underscores the ongoing challenges in developing algorithms capable of nuanced multimodal understanding. Memes often employ intricate visual and textual interplay, cultural references, and contextual subtleties that are difficult for models to fully comprehend. Consequently, there remains significant room for innovation in creating more sophisticated models that can bridge this gap and approach human-level accuracy.

Looking forward, several avenues for future research emerge from our findings. One promising direction is the optimization of ensemble methodologies to reduce computational overhead while maintaining performance gains. Techniques such as model distillation could be explored to create more efficient ensembles without compromising accuracy. Additionally, expanding the diversity and size of training datasets will be crucial in enhancing model generalization and robustness. Collaborative efforts to curate balanced and representative datasets that encompass a wide range of hate speech forms and meme styles will further improve the effectiveness of hate speech detection models.

Another important area for future work is the enhancement of model interpretability. Developing mechanisms to better understand and visualize how BCA-MemeNet makes its predictions can provide valuable insights into the decision-making process, facilitating the identification and mitigation of potential biases. Moreover, incorporating contextual and cultural knowledge into the model training process can enable BCA-MemeNet to better grasp the nuanced expressions of hate speech that vary across different demographic and cultural landscapes.

In conclusion, BCA-MemeNet represents a significant step forward in the automated detection of hateful content within multimodal memes. By integrating advanced bidirectional cross-attention mechanisms and leveraging deep ensemble strategies, our framework achieves superior performance metrics, surpassing existing state-of-the-art models. These accomplishments highlight the potential of sophisticated Transformer architectures in addressing the complex challenge of hate speech detection in the ever-evolving landscape of online content. Nevertheless, the persistent gap between model and human performance emphasizes the need for continued research and innovation to develop more effective and nuanced multimodal hate speech detection systems.