Alencar, A.L.; Lopes, M.D.; Fernandes, A.M.R.; Anjos, J.C.S.; De Paz Santana, J.F.; Leithardt, V.R.Q. Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks. Future Internet2024, 16, 97.
Alencar, A.L.; Lopes, M.D.; Fernandes, A.M.R.; Anjos, J.C.S.; De Paz Santana, J.F.; Leithardt, V.R.Q. Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks. Future Internet 2024, 16, 97.
Alencar, A.L.; Lopes, M.D.; Fernandes, A.M.R.; Anjos, J.C.S.; De Paz Santana, J.F.; Leithardt, V.R.Q. Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks. Future Internet2024, 16, 97.
Alencar, A.L.; Lopes, M.D.; Fernandes, A.M.R.; Anjos, J.C.S.; De Paz Santana, J.F.; Leithardt, V.R.Q. Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks. Future Internet 2024, 16, 97.
Abstract
In the age of social media, images come from unreliable sources that may change their content to fit a narrative. Automated detection of forged images is a complex task, especially considering recent technological advances in image manipulation software. However, in the existing literature, it is possible to identify two general approaches adopted by detection methods, active and passive. Active techniques preemptively act on an image by inserting structures before any manipulation is made that can verify its authenticity. In contrast, passive methods analyze the content of an image in search of traces of manipulation. In this way, this research proposes a novel approach to image manipulation detection, combining two passive methods through neural networks, creating a generalist approach capable of detecting with greater accuracy than the methods that compose it. Furthermore, this work used a combination of four datasets available in the literature for training and evaluation. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. In addition to the performance improvement, the proposed approach’s accuracy variation was lower than the other models.
Keywords
Convolutional neural network; computer vision; deep learning; digital image forensics; image processing
Subject
Computer Science and Mathematics, Information Systems
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.
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