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Recent algorithms for image manipulation detection almost exclusively usedeep network models. These approaches require either dense pixelwisegroundtruth masks, camera ids, or image metadata to train the networks. On onehand, constructing a training set to represent the countless tamperingpossibilities is impractical. On the other hand, social media platforms orcommercial applications are often constrained to remove camera ids as well asmetadata from images. A self-supervised algorithm for training manipulationdetection models without dense groundtruth or camera/image metadata would beextremely useful for many forensics applications. In this paper, we proposeself-supervised approach for training splicing detection/localization modelsfrom frequency transforms of images. To identify the spliced regions, our deepnetwork learns a representation to capture an image specific signature byenforcing (image) self consistency . We experimentally demonstrate that ourproposed model can yield similar or better performances of multiple existingmethods on standard datasets without relying on labels or metadata.

SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization