CAFAD: common acoustic features for adversarial audio detection
Recent studies have shown that deep learning-based automatic speech recognition (ASR) systems are vulnerable to adversarial examples (AEs), which can mislead ASR systems by embedding carefully crafted perturbations into speech signals. These attacks introduce significant challenges to the real-world deployment and reliability of ASR systems. Most existing AE detection methods align inputs by truncating or padding audio samples, which fails to fully use the information in speech and leads to a degradation in detection performance. In addition, few studies have explored the intrinsic characteristics of audio adversarial examples or how these characteristics can be leveraged for robust detection. To address this limitation, this paper proposes CAFAD, a plug-and-play detection framework that combines multi-domain acoustic feature fusion and temporal pyramid matching for variable-length adversarial audio detection. Experiments on LibriSpeech with nine adversarial attacks demonstrate that CAFAD achieves an average detection accuracy of 99.25%, with a false positive rate of 1.00% on benign samples. These results suggest that CAFAD has the potential to be used as a front-end screening module for ASR services, where incoming speech inputs can be inspected before transcription to reduce the risk of adversarial commands in security-sensitive applications such as voice-controlled authentication, smart-home control, and in-vehicle voice interaction. The source code is publicly available at https://anonymous.4open.science/r/CAFAD-59D0.