arXiv:2605.29531v1 Announce Type: cross Abstract: Audio deepfake detection is well-studied as a binary problem, but partially manipulated speech, where a short synthesised segment is spliced into an otherwise genuine utterance, poses a harder and more realistic threat. Detecting such half-truth audio requires not only distinguishing it from real and fully fake speech, but also localising where the manipulation occurs. We present CAFNet, a 576k-parameter architecture that addresses both tasks jointly: it performs ternary classification (real, fully-fake, or half-truth) and regresses the tempora
The proliferation of sophisticated AI audio generation tools necessitates more advanced detection mechanisms to counter rising deepfake threats.
The ability to detect and localize partially manipulated audio is crucial for maintaining trust in digital communications and countering misinformation at scale.
Deepfake detection is evolving beyond binary classification to include the more challenging task of localizing manipulations within an audio segment, addressing a more realistic threat model.
- · Cybersecurity firms
- · Social media platforms
- · Digital forensics
- · Malicious actors
- · Misinformation campaigns
Improved detection capabilities will make it harder to pass off audio deepfakes, particularly 'half-truth' manipulations.
This advancement could drive further innovation in deepfake generation as adversaries seek to evade new detection methods.
The arms race between deepfake generation and detection may lead to a future where audio authenticity requires cryptographic proof or advanced watermarking.
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Read at arXiv cs.LG