VenoMave: Targeted Poisoning Against Speech Recognition

The wide adoption of Automatic Speech Recognition (ASR) remarkably enhanced human-machine interaction. Prior research has demonstrated that modern ASR systems are susceptible to adversarial examples, i.e., malicious audio inputs that lead to misclassification by the victim's model at run time. The research question of whether ASR systems are also vulnerable to data-poisoning attacks is still unanswered. In such an attack, a manipulation happens during the training phase: an adversary injects malicious inputs into the training set to compromise the neural network's integrity and performance. Prior work in the image domain demonstrated several types of data-poisoning attacks, but these results cannot directly be applied to the audio domain. In this paper, we present the first data-poisoning attack against ASR, called VenoMave. We evaluate our attack on an ASR system that detects sequences of digits. When poisoning only 0.17% of the dataset on average, we achieve an attack success rate of 86.67%. To demonstrate the practical feasibility of our attack, we also evaluate if the target audio waveform can be played over the air via simulated room transmissions. In this more realistic threat model, VenoMave still maintains a success rate up to 73.33%. We further extend our evaluation to the Speech Commands corpus and demonstrate the scalability of VenoMave to a larger vocabulary. During a transcription test with human listeners, we verify that more than 85% of the original text of poisons can be correctly transcribed. We conclude that data-poisoning attacks against ASR represent a real threat, and we are able to perform poisoning for arbitrary target input files while the crafted poison samples remain inconspicuous