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cs.SD2026

Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing

Awais Khan, Kutub Uddin, Khalid Malik

Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produce overconfident predictions. T…

cs.SD2026

TRACE: Training-Free Partial Audio Deepfake Detection via Embedding Trajectory Analysis of Speech Foundation Models

Awais Khan, Muhammad Umar Farooq, Kutub Uddin +1

Partial audio deepfakes, where synthesized segments are spliced into genuine recordings, are particularly deceptive because most of the audio remains authentic. Existing detectors…

cs.SD2025

Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study

Kutub Uddin, Muhammad Umar Farooq, Awais Khan +1

The widespread use of generative AI has shown remarkable success in producing highly realistic deepfakes, posing a serious threat to various voice biometric applications, including…

cs.SD2025

SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks

Kutub Uddin, Awais Khan, Muhammad Umar Farooq +1

Audio plays a crucial role in applications like speaker verification, voice-enabled smart devices, and audio conferencing. However, audio manipulations, such as deepfakes, pose sig…

cs.SD2025

Transferable Adversarial Attacks on Audio Deepfake Detection

Muhammad Umar Farooq, Awais Khan, Kutub Uddin +1

Audio deepfakes pose significant threats, including impersonation, fraud, and reputation damage. To address these risks, audio deepfake detection (ADD) techniques have been develop…