A two-branch encoder-decoder network for image tampering localization

Abstract

Tampered images with false information can mislead viewers and pose security issues. Tampering traces in images are difficult to detect. To locate tampering traces effectively, a dual-domain deep-learning-based image tampering localization method based on RGB and frequency stream branches is proposed in this work. The former branch learns and extracts tampered features on the image and content features of the tampered region. The latter branch extracts tampered features from the frequency domain to complement the RGB stream branch. In addition, an attention mechanism is used to integrate the features from both branches at the fusion stage. In the experiments, the F1 score of the proposed method outperformed those of the baselines on the NIST16 dataset (with a 15.3 % improvement), and the AUC score outperformed those of the baselines on the NIST16 and COVERAGE datasets (improvements of 3.9 % and 4.7 %, respectively). This study provides a beneficial alternative to image tampering localization techniques.