arXiv:2503.17577v2 Announce Type: replace-cross Abstract: Deepfakes have emerged as a widespread and rapidly escalating concern in generative AI, spanning images, audio, and videos. Among these, audio deepfakes are particularly alarming due to the growing accessibility of high-quality voice synthesis tools and the ease with which synthetic speech can be distributed through social media and robocalls. Consequently, detecting audio deepfakes is critical for combating the misuse of AI-generated speech. However, real-world audio is often affected by corruptions such as noise, audio modification, a
The rapid advancement and accessibility of generative AI, particularly in voice synthesis, necessitate immediate attention to robust detection mechanisms due to the escalating threat of audio deepfakes.
This research highlights the critical need for effective deepfake detection in real-world conditions, directly impacting information integrity, cybersecurity, and trust in digital communication.
The focus shifts from basic deepfake detection to robustness under real-world corruption, requiring more sophisticated and resilient defense strategies against misuse of AI-generated content.
- · Cybersecurity firms
- · AI ethicists
- · Social media platforms (proactive)
- · Defence tech
- · Disinformation campaigns
- · Companies relying on voice biometrics (without robust liveness detection)
- · News consumers (if detection fails)
Increased investment and research into real-time, robust audio deepfake detection technologies.
Development of new industry standards and regulatory frameworks for verifying audio authenticity in critical applications and public discourse.
A potential 'arms race' between deepfake generation and detection capabilities, continuously pushing the boundaries of AI capabilities and risks.
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Read at arXiv cs.AI