DRL-CLBA: A Clean Label Backdoor Attack for Speech Classification via DDPG Reinforcement Learning
arXiv:2607.01729v1 Announce Type: new 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 i
The continuous development and deployment of deep learning models in sensitive areas like speech classification necessitate sophisticated security research to identify and mitigate emerging threats.
Sophisticated, clean-label backdoor attacks pose a significant threat to the integrity and trustworthiness of AI systems, especially in critical applications where data purity is assumed.
The introduction of DRL-CLBA demonstrates a new, harder-to-detect method for compromising AI models through clean-label backdoors, bypassing previous defenses that relied on identifying poisoned data.
- · AI security researchers
- · Organizations developing robust AI defense mechanisms
- · Ethical hackers proving security vulnerabilities
- · Organizations relying on unhardened speech classification AI
- · Users trusting AI outputs without verification
- · Current backdoor detection methods based on poisoned labels
Increased focus on developing advanced adversarial robustness and clean-label backdoor detection techniques for AI systems.
Potential for new regulations or industry standards for AI security, particularly in sectors using speech classification for sensitive data.
An arms race between AI attackers and defenders, leading to more complex and subtle methods of both attack and defense.
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Read at arXiv cs.AI