Image Tampering Detection Method Based on Swin Transformer and Dense Upsampling Convolution

Abstract

In this paper, we propose a deep neural structure for detecting image copy and paste tampering, including basic copy-move types and copy-move types that overlay post-processing operations. Based on multi-scale Swin Transformer and dense upsampling convolution, we can effectively detect tampered images in small areas. By introducing multi-scale Swin Transformer feature extraction network and integrating global features and local features, the network can adapt to various shapes and sizes of tampered areas, especially in small areas. The effect of tampering is significantly improved. At the same time, the dense up-sampling convolution is used, and the multi-channel filter is used to amplify the down-sampling feature map to restore the input size. The experimental results show that on the public image tamper detection benchmark, this method has significantly improved compared with the comparison method. Compared with BusterNet2019, the accuracy rate, recall rate and value have increased by 16.74 percentage points, 16.48 percentage points and 16.68 percentage points respectively, and the effect has been improved more significantly in small area tampering.