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.
