An efficient USM sharpening detection method for smallsize JPEG image Abstract   USM (Unsharp masking) sharpening is a popular digital image processing operation utilized to pursue better image quality. In recent years, the detection of USM sharpening has attracted attention from image forensics point of view. Some effective methods have been proposed to detect the sharpening forgery in large size image. It is, however, still a challenging task to detect the sharpened images with small size. To address this challenge, an efficient USM sharpening detection algorithm is proposed for smallsize JPEG image. Firstly, the RGB image was converted into the YCrCb image and got the Y channel component. Secondly, the Y channel image was converted into the frequency domain by block DCT transform. Then, the image feature was gotten by difference matrix computation. Finally, the feature set was fed into an SVM classifier for training and testing. The experimental results show the superiority of the proposed method over the existing method in sharpening detection in smallsize images.

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.