arXiv:2606.19579v1 Announce Type: cross Abstract: Audio deepfakes generated by neural text-to-speech and voice-cloning systems threaten speaker verification and public discourse at scale. The core challenge is cross-dataset generalization: detectors trained on one synthesis pipeline collapse on unseen forgeries. We argue that this failure is primarily because of structural synthetic speech artifacts which are multi-timescale trajectory anomalies. Though every existing detector aggregates a fixed-window frame statistics, this misaligns the architecture with the signal. We propose FlowFake, a Li
The rapid advancement and proliferation of neural text-to-speech and voice-cloning systems necessitate more robust detection methods, particularly as their misuse becomes more sophisticated.
The integrity of speaker verification and public discourse is increasingly threatened by scalable audio deepfakes, making effective and generalizable detection critical for trust in digital communication.
This research introduces a novel architectural approach to detect deepfakes, moving beyond fixed-window frame statistics to address multi-timescale trajectory anomalies, potentially improving cross-dataset generalization.
- · Cybersecurity firms
- · Social media platforms
- · Voice authentication services
- · Journalism and fact-checking organizations
- · Deepfake creators
- · Misinformation networks
- · Non-adaptive detection services
Improved deepfake detection tools will emerge, making it harder to deploy synthetic audio for malicious purposes.
Public trust in audio media might incrementally recover as detection capabilities strengthen, though a cat-and-mouse game will persist.
The development of highly robust detection mechanisms could lead to new regulatory frameworks for synthetic media, influencing content creation and distribution.
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Read at arXiv cs.AI