arXiv:2606.10223v1 Announce Type: cross Abstract: Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produce overconfident predictions. To address this, we propose a dual-branch gated fusion framework that pairs XLSR-53 with CORES, a 66-dimensional descriptor that, unlike prior Linear Filter Bank (LFB)-only work, spans cepstral, oscillatory, rhythmic, energy, and spectral dimensions to capture complementary synthesis artifacts. Our analysis shows XLSR-53 remains discriminative in-domain (ID) while CORE
The rapid advancement of generative AI, particularly in audio synthesis, necessitates robust detection and tracing mechanisms to combat misinformation and maintain trust.
Sophisticated deepfake detection is critical for maintaining the integrity of digital communication, verifying content authenticity, and addressing security concerns across various sectors.
The ability to attribute synthetic utterances to their originating systems improves the traceability of malicious content, enabling faster responses and accountability in the deepfake landscape.
- · Cybersecurity firms
- · Digital forensics
- · Social media platforms
- · Law enforcement
- · Deepfake creators
- · Disinformation networks
- · Fraudsters
Improved deepfake attribution directly enhances defenses against audio synthetic media.
Increased trust in digital audio content could lead to broader adoption of AI-generated communications for legitimate purposes.
The arms race between deepfake generation and detection could drive significant R&D investment, shaping the future of AI security.
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Read at arXiv cs.AI