Multitask Deep Learning for Joint Deepfake Speech Detection and Spoofing Attack Attribution
Year: 2026
Type: preprint
Abstract: The rapid advancement of neural speech synthesis and voice conversion technologies has increased the realism of synthetic speech, creating significant challenges for biometric authentication and digital forensics. This study proposes a Multi-Task Deep Learning (MT-DL) framework that jointly addresses binary deepfake speech detection and supervised spoof attribution of known attack classes. The framework combines complementary spectro-temporal representations (MFCC, Log-Mel spectrograms, and delta-MFCC features) with a convolutional front-end and a hybrid Bi-LSTM/Transformer temporal encoder to feed task-specific detection and attribution heads. Evaluated on the ASVspoof 2019 Logical Access dataset using a stratified 60:40 split, the framework achieves a binary detection accuracy of 99.75% (0.48% EER) and a supervised spoof attribution accuracy of 99.57% (99.58% Macro-F1) across known attacks (A01–A06). Unseen attacks (A07–A19) and cross-dataset evaluations on the In-the-Wild dataset further demonstrate robust generalization, though performance sensitivity under severe additive acoustic noise remains a key area for future optimization.
Source: Research Square
Institutions: Sana'a University, Multimedia University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225619375