GIMD-Net: an effective General-purpose image manipulation detection network, even under anti-forensic attacks

Abstract

The digital image information can be easily tampered to harm the integrity of someone. Thus, recognizing the truthfulness and processing history of an image is one of the essential concerns in multimedia forensics. Numerous forensic methods have been developed by researchers with the ability to detect targeted editing operations. But, creating a unified forensic approach capable of detecting multiple image manipulations is still a challenging problem. In this paper, a new GIMD network is designed that exploits local dense connections and global residual learning for better classification by using robust residual dense blocks (RDBs). The network input and high-level hierarchical features produced by proposed residual dense blocks are fused globally for better information flow across the network. The extensive experiment results show that the proposed scheme outperforms the existing state-of-the-art general-purpose forensic schemes even under anti-forensic attacks, when tested on large scale publicly available datasets. Our model offers overall detection accuracies of 95.09% and 97.31% for BOSSBase and Dresden datasets, respectively for multiple image manipulation detection.