arXiv:2607.04848v1 Announce Type: cross Abstract: While audio deepfake detection has advanced significantly, representative detectors show limited generalization to synthetic sound effects. Existing environmental audio datasets such as EnvSDD provide important initial resources, but remain limited in scale and generation provenance for studying isolated sound-effect deepfakes. To support this direction, we present SynSFX, a large-scale corpus of 43374 clips (26452 synthetic, 16922 real) spanning 7 popular text-to-audio models.
The proliferation of sophisticated text-to-audio models necessitates advanced deepfake detection, especially for sound effects which have been less studied, making a dataset like SynSFX timely.
This dataset addresses a critical gap in deepfake detection, moving beyond voice to include sound effects, which is crucial for authenticating digital media and preventing sophisticated audio manipulation.
The availability of SynSFX provides a standardized benchmark and substantially improves the ability to research and develop detectors for synthetic sound effects, enhancing digital forensics and media integrity.
- · AI deepfake detection researchers
- · Audio forensics companies
- · Social media platforms
- · Cybersecurity firms
- · Malicious deepfake creators
- · Platforms with weak audio authentication
- · Undetected audio deepfakes
Improved detection capabilities for deepfake sound effects emerge through research leveraging SynSFX.
The cost and difficulty of creating undetectable audio deepfakes for realistic scenarios, such as fraudulent evidence or misinformation, significantly increases.
Public and institutional trust in digital audio content is partially restored as detection mechanisms become more robust, potentially leading to new media authentication standards.
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Read at arXiv cs.AI