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

2026-08-25

AI Summary

本文提出了一种无需训练的自嵌入隐写术方法，用于检测和恢复部分深度伪造音频，通过在干净语音中嵌入自身压缩表示来实现。

Abstract

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. Experiments on a benchmark dataset show that the proposed approach complements passive defenses. Remarkably, the proposed method operates without any training, providing a robust and data-efficient alternative for partial deepfake detection.

Problem

• partial deepfake speech
• deepfake detection
• audio steganography

Innovation

• Training-Free
• Self-Embedding Steganography
• Partial Deepfake Detection
• Codec-Based Restoration