A Copy-Move Forgery Detection Network Based on Selective Sampling Attention and Low-Cost Two-Step Self-Correlation Calculation
Yuxuan Shi, Shaowei Weng, Lifang Yu, Li Li · IEEE Transactions on Multimedia · 2025
The commonly used standard convolutional layers cannot adaptively adjust the number and locations of sampling points according to the scales and shapes of tampered regions, which increases the difficulty of detecting images containing tampered regions of different sizes. Therefore, the selective sampling attention (SSA) is proposed to automatically learn the number and locations of sampling points as well as the weight of each sampling point within a certain context range of the input feature map through backpropagation, which can help the network better adapt to tampered regions of different scales and shapes. In addition, the self-correlation calculation (SCC), aiming at calculating the similarity between every two feature points in a feature map, necessarily incurs an expensive computational burden when used for high-resolution feature maps. To remedy the problem, the two-step SCC (TS-SCC) with low computation burden is proposed to pick out highly similar regions by means of the feature similarity obtained from low-resolution version of the input feature map, so that the high-resolution version merely needs to calculate the similarity between every two feature points within its high-similarity regions. Finally, to predict the edges and interiors of copy-move tampered regions more precisely, adaptive dual-branch feature fusion module is proposed to employ a lightweight multi-scale atrous convolutional module to adaptively fuse multi-level features before TS-SCC and the correlation features after TS-SCC, thereby improving the detection performance. Combining these three structures, a lightweight, fast, low-cost and high-precision CMFD network, ST-Net, is designed in this paper. Experimental results on four publicly available datasets verify that ST-Net outperforms several related CMFD networks in terms of detection accuracy, number of parameters, computational cost and inference time.