Boosting the Transferability of Audio Adversarial Examples with Acoustic Representation Optimization

2025-03-25

AI Summary

To address the poor cross-model transferability of adversarial examples in automatic speech recognition (ASR) systems, this paper proposes an acoustic representation optimization method: for the first time, adversarial perturbations are constrained within a model-agnostic, low-level robust acoustic feature space, thereby unifying perturbation alignment and transferability. The method is plug-and-play, compatible with mainstream audio adversarial frameworks, and requires no modification to target models. Black-box attack experiments across three state-of-the-art ASR models demonstrate an average 32.7% improvement in transfer success rate, while strictly preserving perceptual fidelity of the original speech. Key contributions include: (1) establishing an acoustic-representation-driven paradigm for enhancing adversarial transferability; (2) achieving synergistic optimization of high transferability and high fidelity; and (3) providing a general, lightweight, and model-agnostic adversarial enhancement solution that requires no access to target model internals.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

With the widespread application of automatic speech recognition (ASR) systems, their vulnerability to adversarial attacks has been extensively studied. However, most existing adversarial examples are generated on specific individual models, resulting in a lack of transferability. In real-world scenarios, attackers often cannot access detailed information about the target model, making query-based attacks unfeasible. To address this challenge, we propose a technique called Acoustic Representation Optimization that aligns adversarial perturbations with low-level acoustic characteristics derived from speech representation models. Rather than relying on model-specific, higher-layer abstractions, our approach leverages fundamental acoustic representations that remain consistent across diverse ASR architectures. By enforcing an acoustic representation loss to guide perturbations toward these robust, lower-level representations, we enhance the cross-model transferability of adversarial examples without degrading audio quality. Our method is plug-and-play and can be integrated with any existing attack methods. We evaluate our approach on three modern ASR models, and the experimental results demonstrate that our method significantly improves the transferability of adversarial examples generated by previous methods while preserving the audio quality.

Problem

• Enhancing transferability of audio adversarial examples across ASR models
• Addressing lack of model-specific information in real-world attack scenarios
• Optimizing perturbations using low-level acoustic representations for consistency

Innovation

• Optimizes acoustic representation for adversarial examples
• Enhances transferability across diverse ASR models
• Plug-and-play integration with existing attack methods