Detecting anti-forensic deepfakes with identity-aware multi-branch networks - Zhu, Mingyu; Long, Jun (Frontiers in Big Data, 2025)
Deepfake detection systems have achieved impressive accuracy on conventional forged images; however, they remain vulnerable to anti-forensic or adversarial samples deliberately crafted to evade detection. Such samples introduce imperceptible perturbations that conceal forgery artifacts, causing traditional binary classifiers—trained solely on real and forged data—to misclassify them as authentic. In this paper, we address this challenge by proposing a multi-channel feature extraction framework combined with a three-class classification strategy. Specifically, one channel focuses on extracting identity-preserving facial representations to capture inconsistencies in personal identity traits, while additional channels extract complementary spatial and frequency domain features to detect subtle forgery traces. These multi-channel features are fused and fed into a three-class detector capable of distinguishing real, forged, and anti-forensic samples. Experimental results on datasets incorporating adversarial deepfakes demonstrate that our method substantially improves robustness against anti-forensic attacks while maintaining high accuracy on conventional deepfake detection tasks.