Fighting Fake News: Image Splice Detection via Learned Self-Consistency

Code

• github.com/minyoungg/selfconsistency
  Officialtf★ 0
• github.com/yizhe-ang/fake-detection-lab
  pytorch★ 25
• github.com/shauryagoel/Image-Splice-Detection
  none★ 0

Abstract

Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded photo EXIF metadata as supervisory signal for training a model to determine whether an image is self-consistent -- that is, whether its content could have been produced by a single imaging pipeline. We apply this self-consistency model to the task of detecting and localizing image splices. The proposed method obtains state-of-the-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool.

Tasks

Reproductions