The Circular U-Net with Attention Gate for Image Splicing Forgery Detection

Abstract

With the advent and rapid development of image tampering technology, it has become harmful to many aspects of our society. Thus, image tampering detection has been increasingly important. Although current forgery detection methods have achieved some success, the scale of the tampered areas in each forgery image are different, and previous methods do not take this into account. In this paper, we believe that the inability of the network to accommodate tampered regions of various sizes is the main reason for the low precision. To address the mentioned problem, we propose a neural network architecture called CAU-Net, which adds residual propagation and feedback, attention gate and Atrous Spatial Pyramid Pooling with CBAM to the U-Net. The Atrous Spatial Pyramid Pooling with CBAM can capture information from multiple scales and adapt to differently sized target areas. In addition, CAU-Net can solve the vanishing gradient issue and suppress the weight of untampered regions, and CAU-Net is an end-to-end network without redundant image processing; thus, it is fast to detect suspicious images. In the end, we optimize the proposed network structure by ablation study, and the experimental results and visualization results demonstrate that our network has a better performance on CASIA and NIST16 compared with state of the art methods.

LPE-Unet: An Improved UNet Network Based on Perceptual Enhancement

“…Like [21,25,[28][29][30][31], the proposed LPE-UNet network in this paper is based on the Ushaped architecture of the UNet network commonly used in medical image segmentation tasks. As shown in Figure 1, the model consists of four parts, encoders, decoders, low-rank perception enhancement modules, and noise optimization modules.…”