Li, L.; Zhang, X.; Chen, K.; Feng, G.; Wu, D.; Zhang, W. Image Steganography and Style Transformation Based on Generative Adversarial Network. Mathematics2024, 12, 615.
Li, L.; Zhang, X.; Chen, K.; Feng, G.; Wu, D.; Zhang, W. Image Steganography and Style Transformation Based on Generative Adversarial Network. Mathematics 2024, 12, 615.
Li, L.; Zhang, X.; Chen, K.; Feng, G.; Wu, D.; Zhang, W. Image Steganography and Style Transformation Based on Generative Adversarial Network. Mathematics2024, 12, 615.
Li, L.; Zhang, X.; Chen, K.; Feng, G.; Wu, D.; Zhang, W. Image Steganography and Style Transformation Based on Generative Adversarial Network. Mathematics 2024, 12, 615.
Abstract
Traditional image steganography conceals secret messages into unprocessed natural images by modifying the pixel value, causing the obtained stego different from the original image in terms of statistical distribution, thereby could be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial Intelligence Generated Content (AIGC) becoming popular in social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image style transformation neural network with steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder-decoder model, and finally generates a styled image containing the secret messages at the same time. An adversarial training technique is applied to enhance the imperceptibility of the generated art-styled stego image with from plain style-transferred images. The lack of the original cover image makes it difficult for the opponent learning a steganalyzer to identify the stego. The recommended approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of 3 bits per pixel for a color image, according to experimental results.
Keywords
Generative Adversarial Network (GAN); image steganography; style transfer
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
