A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World

TL;DR

This paper introduces a new paradigm for face forgery detection in multi-domain scenarios, emphasizing definitive real/fake judgments on domain-unspecified images, and proposes a model-agnostic framework to enhance detection robustness.

Contribution

It defines the MID-FFD paradigm and proposes DevDet, a framework that amplifies real/fake differences to improve detection accuracy in multi-domain, real-world conditions.

Findings

DevDet improves real/fake prediction accuracy in MID-FFD scenarios.

The framework maintains generalization ability to unseen data.

Experiments show superiority over existing methods.

Abstract

Existing methods for deepfake detection aim to develop generalizable detectors. Although "generalizable" is the ultimate target once and for all, with limited training forgeries and domains, it appears idealistic to expect generalization that covers entirely unseen variations, especially given the diversity of real-world deepfakes. Therefore, introducing large-scale multi-domain data for training can be feasible and important for real-world applications. However, within such a multi-domain scenario, the differences between multiple domains, rather than the subtle real/fake distinctions, dominate the feature space. As a result, despite detectors being able to relatively separate real and fake within each domain (i.e., high AUC), they struggle with single-image real/fake judgments in domain-unspecified conditions (i.e., low ACC). In this paper, we first define a new research paradigm named Multi-In-Domain Face Forgery Detection (MID-FFD), which includes sufficient volumes of real-fake domains for training. Then, the detector should provide definitive real-fake judgments to the domain-unspecified inputs, which simulate the frame-by-frame independent detection scenario in the real world. Meanwhile, to address the domain-dominant issue, we propose a model-agnostic framework termed DevDet (Developer for Detector) to amplify real/fake differences and make them dominant in the feature space. DevDet consists of a Face Forgery Developer (FFDev) and a Dose-Adaptive detector Fine-Tuning strategy (DAFT). Experiments demonstrate our superiority in predicting real-fake under the MID-FFD scenario while maintaining original generalization ability to unseen data.