2025/11/23 by Hao Shen, Shen, Hao, Jikang Cheng +9
Computer Science · #Confusion #Digital Media Forensic Detection #Domain (mathematical analysis) #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #Generative grammar #Generator (circuit theory) #Replay attack #Sample (material) #Weighting
paper · pdf · open access · doi:10.48550/arxiv.2511.18436
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/11/23 · openalex created_date 2025/11/27 · openalex updated_date 2026/07/28
The rapid advancement of face generation techniques has introduced an increasing variety of forgery methods, making incremental deepfake detection essential for maintaining robust detectors. While generative replay provides a promising solution to alleviate catastrophic forgetting without storing historical data, its effectiveness is hindered by domain confusion between generated samples and real data. We observe that replay samples fall into two categories: when the replay generator closely resembles the newly introduced forgery model, generated real samples overlap with the fake domain and become domain-risky; when the generator differs significantly, generated samples maintain clearer domain separation and can be treated as domain-safe. To address this challenge, we propose a Dual Confusion-Aware REgularization strategy, termed Dual-CARE. A Domain-aware Confusion Score (DC Score) is introduced to quantify domain confusion and dual-modulate the optimization of both replay generators and the incremental detector. Guided by DC Score, replay generators are updated to better approximate previous-task distributions, while the detector adopts different supervision strategies: domain-safe samples are directly supervised, whereas domain-risky samples are regulated using a Relative Separation Loss to balance supervision and confusion. Extensive experiments demonstrate that Dual-CARE effectively exploits generative replay and improves incremental deepfake detection under evolving forgery scenarios.