ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization

TL;DR

ReLoc introduces a restoration-assisted framework that enhances the robustness of image tampering localization against post-processing like JPEG compression by recovering high-quality images to better detect tampered regions.

Contribution

The paper proposes a novel framework combining image restoration and tampering localization, optimized jointly with a forensics-oriented objective, to improve robustness against post-processing.

Findings

Significantly improves robustness against JPEG compression.

Restoration module is transferable across different localization modules.

Joint training stabilizes performance and enhances tampering detection.

Abstract

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing image tampering localization methods, however, suffer from severe performance degradation when the tampered images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become a bottleneck for the practical applications of image tampering localization techniques. In order to address this issue, this paper proposes a novel restoration-assisted framework for image tampering localization (ReLoc). The ReLoc framework mainly consists of an image restoration module and a tampering localization module. The key idea of ReLoc is to use the restoration module to recover a high-quality counterpart of the distorted tampered image, such that the distorted tampering traces can be re-enhanced, facilitating the tampering localization module to identify the tampered regions. To achieve this, the restoration module is optimized not only with the conventional constraints on image visual quality but also with a forensics-oriented objective function. Furthermore, the restoration module and the localization module are trained alternately, which can stabilize the training process and is beneficial for improving the performance. The proposed framework is evaluated by fighting against JPEG compression, the most commonly used post-processing. Extensive experimental results show that ReLoc can significantly improve the robustness against JPEG compression. The restoration module in a well-trained ReLoc model is transferable. Namely, it is still effective when being directly deployed with another tampering localization module.