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

TL;DR

This paper introduces a self-supervised learning method for image splicing detection and localization that does not require dense labels or metadata, using frequency domain analysis to learn image-specific signatures.

Contribution

The paper presents a novel self-supervised approach that leverages frequency transforms to train manipulation detection models without labeled data or metadata.

Findings

Achieves comparable or better performance than existing methods on standard datasets.

Does not rely on dense groundtruth masks or camera metadata.

Effective in real-world scenarios with limited supervision.

Abstract

Recent algorithms for image manipulation detection almost exclusively use deep network models. These approaches require either dense pixelwise groundtruth masks, camera ids, or image metadata to train the networks. On one hand, constructing a training set to represent the countless tampering possibilities is impractical. On the other hand, social media platforms or commercial applications are often constrained to remove camera ids as well as metadata from images. A self-supervised algorithm for training manipulation detection models without dense groundtruth or camera/image metadata would be extremely useful for many forensics applications. In this paper, we propose self-supervised approach for training splicing detection/localization models from frequency transforms of images. To identify the spliced regions, our deep network learns a representation to capture an image specific signature by enforcing (image) self consistency . We experimentally demonstrate that our proposed model can yield similar or better performances of multiple existing methods on standard datasets without relying on labels or metadata.