Abstract
To reduce the difficulty of image forensics on forgery images, in this paper, we present an efficient end-to-end deep learning approach using Residual Structure and Attention Mechanism (RA-Net) for image copy-move forgery detection (CMFD). The RA-Net can locate the forged areas and corresponding genuine areas, and it is composed of two modules, Residual Feature Extraction module (RFEM) and Feature Matching & Up-sampling module (FMUM). RFEM is designed to extract deep feature maps, which enriches the combination of gradient information and attention mechanism that focuses the attention of RA-Net to the forged areas. The FMUM assists RA-Net is used to detect copy-move forgery areas and return the previous output to the size of the input image for analysis and visualization of the results. Furthermore, we create a RANet-CMFD dataset for the training, the way to generate RA-Net-CMFD dataset could help solve the problem of not having enough dataset in some research areas. Otherwise, comparison results show that our model can achieve satisfied performance on CoMoFoD dataset at the pixel level, and performs superior than the compared methods.
This work was supported by the National Natural Science Foundation of China (Grant No. 61902448), the Science and Technology Development Fund of Macau SAR (Grant number 0045/2022/A), and the Research project of the Macao Polytechnic University (Project No. RP/FCA-12/2022).
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 61902448), the Science and Technology Development Fund of Macau SAR (Grant number 0045/2022/A), and the Research project of the Macao Polytechnic University (Project No. RP/FCA-12/2022).
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Zhao, K., Yuan, X., **e, Z., Huang, G., Feng, L. (2023). RA-Net: A Deep Learning Approach Based on Residual Structure and Attention Mechanism for Image Copy-Move Forgery Detection. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14263. Springer, Cham. https://doi.org/10.1007/978-3-031-44204-9_31
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