DRL-CLBA: A Clean Label Backdoor Attack for Speech Classification via DDPG Reinforcement Learning

• URL: http://arxiv.org/abs/2607.01729v1
• Date: Thu, 02 Jul 2026 05:34:20 GMT
• Title: DRL-CLBA: A Clean Label Backdoor Attack for Speech Classification via DDPG Reinforcement Learning
• Abstract summary: We propose DRL-CLBA, a novel clean label backdoor attack for speech classification.<n>We use deep audio steganography to embed sample-specific triggers into source audio, creating feature-space anchors.<n>We show that DRL-CLBA achieves a high attack success rate, effectively bypassing some backdoor defenses.
• Score: 1.2373172839910807
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Deep learning models for speech classification are vulnerable to backdoor attacks, where malicious triggers cause misclassification at inference time. While sample-specific attacks can bypass many defenses, they often rely on poisoned label attack, making them detectable via manual data defense. In this paper, we propose DRL-CLBA, a novel clean label backdoor attack for speech classification that leverages Deep Deterministic Policy Gradient (DDPG) reinforcement learning. We also utilize deep audio steganography to embed sample-specific triggers into source audio, creating feature-space anchors. The proposed reinforcement learning framework effectively optimizes target samples toward trigger-bearing anchor points in the model's deep latent space, enabling label-migration-free poisoning of target samples. Experimental results across three datasets and four different DNNs demonstrate that DRL-CLBA achieves a high attack success rate, effectively bypassing some backdoor defenses. The attack demonstrates strong resistance against fine-tuning, pruning, and spectral signature defenses, exposing critical vulnerabilities in speech-controlled systems.

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