A Passive Scheme for Tampering Detection Based on Quantization Table Estimation In this paper, we proposed a passive scheme to achieve image tampering detection. To correctly estimate the quantization table, each AC DCT coefficient is classified into different types and then the corresponding quantization stepsize is adaptively measured from its power spectrum density (PSD) and PSDu0027s Fourier transform. Based on the quantization table estimation, the proposed scheme is composed of prescreening, candidate region selection, and tampering region identification. The prescreening is developed to decide whether an image had been JPEG compressed. To select candidate regions for estimating quantization table, a seed region is first selected by finding a suitable region by removing suspicious regions. Based on the seed region, the candidate region can be obtained by suitably merging other regions into the seed. To avoid merging the suspect regions, a candidate region refinement is performed in the region growing. After estimating the quantization table from the candidate region, a MLR classifier based on the inconsistency of quantization table is exploited to identify tampered regions block by block. The experimental results demonstrate that our proposed scheme can not only estimate the quantization table but also identify tampered regions well.

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.