Depthwise Separable Convolutional Neural Network for Image Forensics Generalpurpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can automatically learn effective and robust features related to image forensics with high parameter efficiency. In this paper, we propose a depthwise separable convolutional neural network (CNN) for the simultaneous detection of eleven types of image manipulations in image patches. Different from the previous CNNs based on standard convolution, depthwise separable convolution is introduced in the proposed CNN to adaptively extract forensicsrelated features from image patches with better parameter efficiency. When compared with four stateoftheart methods, experiments demonstrate that the proposed CNN architecture can achieve better performance, e.g., the improvement in terms of accuracy in the detection of 32 xc3x97 32 images is up to 7.33%. It also achieves significantly better overall performance for different databases and better robustness against JPEG compression.

Deep Multiscale Discriminative Networks for Double JPEG Compression Forensics As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is selfevident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive laborconsuming research and derivation is required. In this paper, we propose a novel image tampering detection method based on deep multiscale discriminative networks (MSDNets). The multiscale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (QF1) is higher than the second one (QF2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with stateoftheart approaches.