Face X-ray for More General Face Forgery Detection

TL;DR

This paper introduces face X-ray, a novel image representation that detects face forgery by revealing blending boundaries, demonstrating effectiveness across various manipulation techniques without relying on specific artifact knowledge.

Contribution

The paper proposes face X-ray, a general forgery detection method based on blending boundary detection that works across multiple face manipulation techniques without prior training on fake images.

Findings

Effective detection of unseen face manipulation techniques

Outperforms existing methods on generalization tests

Does not rely on specific artifact patterns

Abstract

In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We observe that most existing face manipulation methods share a common step: blending the altered face into an existing background image. For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms. Face X-ray is general in the sense that it only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique. Indeed, the algorithm for computing face X-ray can be trained without fake images generated by any of the state-of-the-art face manipulation methods. Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop.