arXiv:2602.05670v2 Announce Type: replace-cross Abstract: Advances in AIGC technologies have enabled the synthesis of highly realistic audio deepfakes capable of deceiving human auditory perception. Although numerous audio deepfake detection (ADD) methods have been developed, most rely on local temporal/spectral features or pairwise relations, overlooking high-order interactions (HOIs). HOIs capture discriminative patterns that emerge from multiple feature components beyond their individual contributions. We propose HyperPotter, a hypergraph-based framework designed to capture high-order relat
The rapid advancement of AIGC technologies necessitates immediate sophisticated defense mechanisms against generated deepfakes as their realism improves.
This development is crucial for maintaining trust in digital audio, impacting sectors from forensic analysis to national security and public discourse.
Deepfake detection methods are evolving beyond simple features to incorporate high-order interactions, making them more resilient against increasingly realistic AI-generated content.
- · Cybersecurity firms
- · Forensic specialists
- · Digital media platforms
- · Malicious deepfake creators
- · AI-powered disinformation campaigns
Improved detection capabilities will make it harder for audio deepfakes to deceive humans and automated systems.
This will drive an arms race between deepfake generation and detection, accelerating AI research in both areas.
Increased confidence in audio authenticity could lead to new applications requiring verified vocal identity, such as secure financial transactions or authentication systems.
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Read at arXiv cs.AI