• Baoxiang Jiang Xiamen University, Xiamen, China
  • Jingbo Xia Xiamen University Tan Kah Kee College, Zhangzhou, China
  • Yanting Wang Xiamen University, Xiamen, China
Keywords: Image Manipulation Detection, Sobel Residual, Central Differential Convolution


Recently, most successful image manipulation detection methods have been based on convolutional neural networks (CNNs). Nevertheless, Existing CNN methods have limited abilities. CNN-based detection networks tend to extract signal features strongly related to content. However, image manipulation detection tends to extract weak signal features that are weakly related to content. To address this issue, We propose a novel Sobel residual neural network with adaptive central difference convolution, an extension of the classical U-Net architecture, for image manipulation detection. Adaptive central differential convolution can capture the essential attributes of an image by gathering intensity and gradient information. Sobel residual gradient block can capture forgery edge discriminative details. Extensive experimental results show that our method can significantly improve the accuracy of localising the forged region compared with the state-of-the-art methods.


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How to Cite
Jiang, B., Xia, J., & Wang, Y. (2021). SRU-NET: SOBEL RESIDUAL U-NET FOR IMAGE MANIPULATION DETECTION. International Journal of Advanced Computer Technology, 10(5), 01-06. Retrieved from