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As JPEG is the most widely used image format, the importance of tamperingdetection for JPEG images in blind forensics is self-evident. In this area,extracting effective statistical characteristics from a JPEG image forclassification remains a challenge. Effective features are designed manually intraditional methods, suggesting that extensive labor-consuming research andderivation is required. In this paper, we propose a novel image tamperingdetection method based on deep multi-scale discriminative networks (MSD-Nets).The multi-scale module is designed to automatically extract multiple featuresfrom the discrete cosine transform (DCT) coefficient histograms of the JPEGimage. This module can capture the characteristic information in differentscale spaces. In addition, a discriminative module is also utilized to improvethe detection effect of the networks in those difficult situations when thefirst compression quality (QF1) is higher than the second one (QF2). A specialnetwork in this module is designed to distinguish the small statisticaldifference between authentic and tampered regions in these cases. Finally, aprobability map can be obtained and the specific tampering area is locatedusing the last classification results. Extensive experiments demonstrate thesuperiority of our proposed method in both quantitative and qualitative metricswhen compared with state-of-the-art approaches.

Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics