Paradoxical Role of Adversarial Attacks: Enabling Crosslinguistic Attacks and Information Hiding in Multilingual Speech Recognition

Wenjie Zhang, Zhihua Xia, Bin Ma, Diqun Yan

Abstract: With the rise of automatic speech recognition (ASR) research and practical applications, enabling adversarial attacks on ASR systems via subtle perturbations has become a priority. Most prior research has focused on single-language, single-model ASR systems. However, multilingual ASR systems hold opportunities for crosslinguistic attacks and covert message transmission. This letter introduces a new approach for crosslinguistic adversarial attacks in multilingual ASR, focusing on information hiding. For example, in military settings, adversarial examples applied to eavesdropping devices can encode messages detectable only by friendly devices, leaving adversaries, even with identical methods, unable to access them. This letter examines multilingual ASR system properties and introduces a crosslinguistic adversarial example with minimal perturbation, allowing friendly classifiers to extract hidden information while being undetectable by hostile classifiers. The experimental results on 5 models and 5 datasets show that the proposed method achieves a success rate of over 90% and an SNR close to 40 dB.