Backdoor Attacks on Speech Emotion Recognition via TTS-Generated Poisoning
Speech Emotion Recognition (SER) systems increasingly leverage self-supervised acoustic representations, yet their vulnerability to training-time attacks remains largely underexplored. This paper presents the first systematic study of poisoning-based backdoor attacks on SER, with a focus on threats enabled by text-to-speech (TTS) generated audio. We introduce a stealthy, low-energy acoustic trigger that can be embedded imperceptibly into both natural and synthetic speech, enabling scalable and consistent poisoning. Our experiments demonstrate that SER models can be reliably compromised with high attack success rates under low poisoning ratios, while maintaining near-clean performance on b...
| Authors | Yongbin Huang, Xihao Xie, Jia Zhang |
|---|---|
| Primary category | cs.SD |
| Published | 19 June 2026 |
| Last updated at source | 1 September 2026 |
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