A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding

Experiments on a benchmark dataset show that the proposed approach complements passive defenses. It comes from a paper posted to arXiv on 22 September 2026. 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 itself, enabling post-hoc extraction of reference content. We demonstrate how existing audio steganography methods can be repurposed to support detection of partial deepfakes through codec-based restoration. Remarkably, the proposed method operates without any training, providing a robust and data-efficient alternative for partial deepfake detection.

Why it counts

Experiments on a benchmark dataset show that the proposed approach complements passive defenses.

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What is not known

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