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Cross-representation transferable adversarial examples generation for audio classification

Year: 2025
Type: article
Abstract: Adversarial examples have been used as an important tool for detecting vulnerabilities in deep neural networks. The unique property of transferability enables them to deceive black-box deep neural network models. Transfer-based attack methods for audio classification have recently attracted extensive discussion. However, in the audio domain, existing transfer-based methods overlooked the transferability of adversarial audio examples from 1D waveform to 2D Mel-spectrogram representation. It was observed that adversarial examples generated against a raw-waveform surrogate model could not successfully attack Mel-spectrogram target models. A method for generating cross-representation transferable adversarial examples for audio classification was proposed. Specifically, losses from two pre-trained surrogate models accepting different representations were linearly combined to train generators for crafting adversarial audio examples with high cross-representation and cross-model transferability. Furthermore, the predictive discrepancies between the two surrogate models were minimized to enhance cross-representation transferability. Additionally, the generator's output was used to train these surrogate models adversarially, which significantly enhanced the robustness of the surrogate models and consequently boosted the cross-model transferability of adversarial examples. Extensive experiments were carried out on the Urban Sound8k and ShipsEar datasets. The results demonstrate that this approach could improve the cross-representation transferability of adversarial examples and significantly outperform existing transfer-based attacks.
Authors: TIAN Zilin, LONG Yunfei, TIAN Ye, ZHANG Liguo
Language: English
Open Access status: green
OpenAlex ID: W7018222840
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