Not All Attacks Are Learned Equally in Speech Deepfake Detection

Abstract

Speech deepfake detection (SDD) models are trained on multi-attack datasets containing diverse spoofing systems, such as text-to-speech (TTS) and voice conversion (VC). In standard classifier training on multi-attack datasets, all attacks are treated as one spoofed class, and performance is reported using overall Equal Error Rate (EER). This aggregate view obscures how individual attacks shape learning and generalization. To better understand this attack-level behavior, we first balance TTS and VC exposure using sample and attack omission. We then measure attack-wise EER at inference and analyze attack-wise training loss and predictive entropy to characterize optimization. Results show that attacks contribute unequally: some attacks have high EER sensitivity and concentrated entropy with low loss, indicating strong influence on the decision boundary. We define these as high-impact attacks. To reduce uneven generalization across attacks, we propose a replay-regularized, attack-aware curriculum that steps exposure based on measured attack influence. Experiments on ASVspoof 2019, 2021, ASVspoof 5, and Fake-or-Real show improved overall robustness and reduced attack-level imbalance compared with standard multi-attack training.