2025-01-14-wang25f.md

title	booktitle	year	volume	series	month	publisher	pdf	url	openreview	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Diffusion-based Adversarial Attack to Automatic Speech Recognition	Proceedings of the 16th Asian Conference on Machine Learning	2025	260	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v260/main/assets/wang25f/wang25f.pdf	https://proceedings.mlr.press/v260/wang25f.html	rmPwZmVaSp	Recent studies have exposed the substantial vulnerability of voice-activated smart devices to adversarial examples, predominantly targeting the robustness of automatic speech recognition (ASR) systems. Most of adversarial examples generated by introducing adversarial perturbations within the $l_p$ norm bounds to benign audio inputs. However, these attacks are constrained by the parametric bounds of perturbations or the features of disturbance, which limits their effectiveness. To improve the acoustic realism of adversarial examples and enhance attack performance, we propose a novel attack framework called Diffusion-based Adversarial Attack, leveraging DiffVC, a diffusion-based voice conversion model, to map audio to a latent space and employing Adversarial Latent Perturbation (ALP) to embed less perceptible and more robust perturbations. Extensive evaluations demonstrate that our method enhances targeted attack performance. Notably, the Word Error Rate (WER) has shown an average increase of 101 absolute points over clean speech audio and 25 absolute points over C&W attack. Additionally, the Success Rate (SR) has achieved an average increase of 11 absolute points over the C&W attack and 16 absolute points over SSA attack. Additionally, our approach also stands out for its high audio quality and efficiency.	inproceedings	2640-3498	wang25f	Diffusion-based Adversarial Attack to Automatic Speech Recognition	889	904	889-904	889	false	Nguyen, Vu and Lin, Hsuan-Tien	given family Vu Nguyen given family Hsuan-Tien Lin	Wang, Ying and Luo, Yuchuan and Fu, Shaojing and Qiu, Zhenyu and Liu, Lin	given family Ying Wang given family Yuchuan Luo given family Shaojing Fu given family Zhenyu Qiu given family Lin Liu	2025-01-14		Proceedings of the 16th Asian Conference on Machine Learning	inproceedings	date-parts 2025 1 14