Harder or Different? Understanding Generalization of Audio Deepfake Detection
This addresses the generalization challenge in audio deepfake detection for security applications, showing that current trends of increasing model capacity may be ineffective, making it incremental in its critique of existing approaches.
The study investigated whether poor generalization in speech deepfake detection is due to newer deepfakes being harder to detect or fundamentally different, finding that the performance gap is primarily attributed to differences between models rather than increased difficulty, with the hardness component being negligible in experiments using ASVspoof databases.
Recent research has highlighted a key issue in speech deepfake detection: models trained on one set of deepfakes perform poorly on others. The question arises: is this due to the continuously improving quality of Text-to-Speech (TTS) models, i.e., are newer DeepFakes just 'harder' to detect? Or, is it because deepfakes generated with one model are fundamentally different to those generated using another model? We answer this question by decomposing the performance gap between in-domain and out-of-domain test data into 'hardness' and 'difference' components. Experiments performed using ASVspoof databases indicate that the hardness component is practically negligible, with the performance gap being attributed primarily to the difference component. This has direct implications for real-world deepfake detection, highlighting that merely increasing model capacity, the currently-dominant research trend, may not effectively address the generalization challenge.