FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videos

TL;DR

FakeRadar is a deepfake detection framework that uses large-scale pretrained models and outlier probing techniques to improve detection of unseen forgery methods and enhance cross-domain generalization.

Contribution

It introduces Forgery Outlier Probing and Outlier-Guided Tri-Training to better detect unknown deepfake manipulations, addressing limitations of existing methods.

Findings

Outperforms existing methods on benchmark datasets

Shows strong cross-domain generalization capabilities

Effectively detects emerging manipulation techniques

Abstract

In this paper, we propose FakeRadar, a novel deepfake video detection framework designed to address the challenges of cross-domain generalization in real-world scenarios. Existing detection methods typically rely on manipulation-specific cues, performing well on known forgery types but exhibiting severe limitations against emerging manipulation techniques. This poor generalization stems from their inability to adapt effectively to unseen forgery patterns. To overcome this, we leverage large-scale pretrained models (e.g. CLIP) to proactively probe the feature space, explicitly highlighting distributional gaps between real videos, known forgeries, and unseen manipulations. Specifically, FakeRadar introduces Forgery Outlier Probing, which employs dynamic subcluster modeling and cluster-conditional outlier generation to synthesize outlier samples near boundaries of estimated subclusters, simulating novel forgery artifacts beyond known manipulation types. Additionally, we design Outlier-Guided Tri-Training, which optimizes the detector to distinguish real, fake, and outlier samples using proposed outlier-driven contrastive learning and outlier-conditioned cross-entropy losses. Experiments show that FakeRadar outperforms existing methods across various benchmark datasets for deepfake video detection, particularly in cross-domain evaluations, by handling the variety of emerging manipulation techniques.