arXiv:2601.03615v2 Announce Type: replace Abstract: Audio Language Models (ALMs) offer a promising shift towards explainable audio deepfake detections (ADD), moving beyond \textit{black-box} classifiers by providing transparency to their predictions via reasoning traces. However, such reasoning may not support the model predictions, reflecting poor coherence, or, worse, may rationalize incorrect predictions with plausible but misleading explanation. Moreover, the behavior of ALM reasoning under adversarial attacks remains under-explored, raising questions about the practical reliability of suc
The proliferation of sophisticated AI-generated content necessitates more robust and explainable detection methods, leading to research into the reliability of Audio Language Models for deepfake detection.
Reliable deepfake detection is crucial for maintaining trust in digital communication and forensic integrity, especially as synthetic media becomes indistinguishable from reality.
The focus in deepfake detection is shifting towards evaluating the coherence and adversarial robustness of AI-generated explanations, rather than just the detection accuracy itself.
- · Digital forensics companies
- · Cybersecurity firms
- · Social media platforms
- · Audio Deepfake Developers (those who learn to evade detection with robust reason
- · Black-box deepfake detection systems
- · Malicious actors relying on undetectable audio deepfakes
- · Organizations vulnerable to sophisticated misinformation
Increased research and development into explainable and robust AI for deepfake detection.
A cat-and-mouse game where deepfake generation and detection advance in sophistication, focusing specifically on the explainability and adversarial resilience of their models.
Potential for an 'explanation arms race' where models generating deepfakes also generate plausible but misleading reasoning to evade detection, further complicating trust in AI-generated content and explanations.
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Read at arXiv cs.CL