Forensic analysis of manipulation chains: A deep residual network for detecting JPEG-manipulation-JPEG

Abstract

Due to the proliferation of image editing software, the problem of identifying doctored photos has risen to prominence in information forensics. Even though existing image processing history detection techniques could provide reasonably acceptable outcomes on publicly available databases, most of them generally fail to accomplish this when the doctored images are lossy compressed, which is commonly accomplished in social media. To tackle this issue, a dual-stream residual network (ReMReNet) using ResNet as a backbone is proposed for JPEG-resistant image operator chain detection. ReMReNet consists of noise residual extraction (NRE) and compression feature extraction (CFE) streams for better results against JPEG compression. The CFE stream is trained on discrete cosine transform (DCT) residual coefficients by producing clues left by JPEG-agent images with generic JPEG compression artefacts. In addition, the NRE stream is utilized to extract noise residuals to suppress the image content and enhance the manipulation clues. Hence, the domain transformation between uncompressed and post-JPEG compressed images is reduced by training the backbone network with a domain adaption approach, which helps forensic investigators pinpoint the exact location of the tampering. The proposed framework outperforms other state-of-the-art algorithms in image multiple manipulations and operator chain detection, notably for lossy JPEG compression, as shown by extensive experimental findings on numerous heterogeneous datasets.