Abstract
In recent years, significant progress has been made in image forgery localization technology, but there are still shortcomings in accuracy and robustness, especially for different types of forgeries. Currently, convolutional neural networks are the mainstream forgery localization technology, but the local receptive field of convolutional operations limits the accuracy of the model. In this paper, an image forgery localization neural network model is proposed, which uses the ringed residual structure to extract features better and multiple attention mechanisms to focus on important features better. The method can effectively handle various common forgeries types, such as copy-paste, region replacement, and local modification, and can also overcome the problem of the local receptive field of convolutional operations to achieve long-distance feature dependencies. The authors conducted experiments on multiple datasets to verify and compare the proposed method with existing methods. The experimental results show that the method achieved excellent performance on different datasets, demonstrating its effectiveness and robustness.
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Guo, Y., Jiang, S. (2023). A Neural Network Model with the Ringed Residual Block and Attention Mechanism for Image Forgery Localization. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14358. Springer, Cham. https://doi.org/10.1007/978-3-031-46314-3_8
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DOI: https://doi.org/10.1007/978-3-031-46314-3_8
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