SOURCE-LINKED INTELLIGENCE
A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography
Partial deepfake speech, where only limited segments of an utterance are synthesized or manipulated, poses a significant challenge to existing deepfake detection systems. As the proportion of spoofed regions decreases, passive detectors become increasingly unreliable, and accurate detection and restoration remain challenging. In this paper, we revisit audio steganography from a new perspective and propose its use as a proactive defense against partially deepfaked audio. In particular, we consider a self-embedding strategy in which a clean speech signal embeds a compressed representation of its
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T01:45:35.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.