XAI-Grounded Explanation Generation for Speech Deepfake Detection with Training-Free Multimodal Large Language Models
arXiv:2606.16137v1 Announce Type: new Abstract: Speech deepfake detection (SDD) systems require trustworthy explanations for reliable decision-making. Existing explanation ways mainly fall into two categories. Traditional explainable AI (XAI), such as gradient-based attribution, produces low-level attribution signals tightly coupled with model decisions, and harder to be understood by human than natural language explanations. Meanwhile, large language model (LLM)-based explanation generation often produces generic and ungrounded descriptions due to the lack of heuristic evidence and task-speci
The proliferation of deepfake technology necessitates more robust and understandable detection systems, driving research into explanation generation that bridges technical AI outputs with human comprehension.
Improved explainability for deepfake detection fosters trust in AI systems critical for combating misinformation and maintaining digital security across various sectors.
The development of XAI-grounded explanation generation for speech deepfake detection makes these systems more transparent and auditable for human users and decision-makers.
- · AI ethics and safety researchers
- · Digital forensics and security firms
- · Social media platforms
- · Governments and regulatory bodies
- · Malicious deepfake creators
- · Generic LLM-based explanation generators
- · Traditional, low-level XAI methods
More effective and trusted deepfake detection systems combat the spread of synthetic media manipulation.
Increased public and institutional confidence in AI-driven content verification tools leads to broader adoption and reliance.
The development of human-interpretable AI explanations sets a precedent for AI system design, influencing future regulatory frameworks and user expectations across different AI applications.
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Read at arXiv cs.CL