Low- and Semantic-level Cues for Forensic Splice Detection

Image forensics is increasingly of interest, due to the deluge of online photo-sharing platforms and high-quality image editing software. While advances in computer vision enable these editing tools, they also provide a means by which to detect such tampering. Recent work in large-scale image phylogeny, for instance, aims to infer relationships between multiple images which may have contributed to a manipulation. A key task within this scope is detecting splicing between a pair of images, which we address with a combination of low-and semantic-level cues in order to provide fast detection with the false alarm rate demanded by large image collections. We show that, while deep learning approaches contribute to these ends, traditional feature-based detection still form the basis of a useful detector, providing scale invariance without incurring the complexity associated with deep networks. Our method out-performs the state-of-the-art approach based on deep learning, measured on a challenge dataset for splice detection, and can also be used for detection of copy-move manipulations.