Detecting double JPEG compression and its related anti-forensic operations with CNN

Detecting double JPEG compression is important to forensic experts in identifying the originality and authenticity of images. However, there are some anti-forensic techniques which can evade existing double compression detectors. It is desirable to design a unified approach to address the issues of JPEG forensics and counter-anti-forensics simultaneously, but existing hand-crafted feature based methods and deep learning based methods may fail to satisfy the requirement. In this paper, we present a data-driven approach by using a convolutional neural network (CNN) which takes input from both raw JPEG DCT coefficients and decompressed image pixels. Expert knowledge about JPEG characteristics is incorporated in the CNN design by exploring the intricate relations both within and among DCT subbands and by looking for spatial artifacts both within and among JPEG grids. The CNN is capable of learning deep representations from training data and thus can effectively detect double JPEG compression and its related anti-forensic operations together. The end-to-end CNN that takes into account the information from both DCT domain and spatial domain, shows outstanding performance when compared to prior arts in the experiments. It shows a promising way to address counter-anti-forensic issues without designing specific features for each anti-forensic operation.