Matías Pizarro

Matías Pizarro, Raghavan Narasimhan, Asja Fischer

With the increasing deployment of automated and agentic systems, ensuring the adversarial robustness of automatic speech recognition (ASR) models has become critical. We observe that changing the precision of an ASR model during inference reduces the likelihood of adversarial attacks succeeding. We take advantage of this fact to make the models more robust by simple random sampling of the precision during prediction. Moreover, the insight can be turned into an adversarial example detection strategy by comparing outputs resulting from different precisions and leveraging a simple Gaussian classifier. An experimental analysis demonstrates a significant increase in robustness and competitive detection performance for various ASR models and attack types.

Matías Pizarro, Mike Laszkiewicz, Shawkat Hesso et al.

As speech generation technologies continue to advance in quality and accessibility, the risk of malicious use cases, including impersonation, misinformation, and spoofing, increases rapidly. This work addresses this threat by introducing a simple, training-free, yet effective approach for detecting AI-generated speech and attributing it to its source model. Specifically, we tackle three key tasks: (1) single-model attribution in an open-world setting, where the goal is to determine whether a given audio sample was generated by a specific target neural speech synthesis system (with access only to data from that system); (2) multi-model attribution in a closed-world setting, where the objective is to identify the generating system from a known pool of candidates; and last but not least (3) detection of synthetic versus real speech. Our approach leverages standardized average residuals-the difference between an input audio signal and its filtered version using either a low-pass filter or the EnCodec audio autoencoder. We demonstrate that these residuals consistently capture artifacts introduced by diverse speech synthesis systems, serving as distinctive, model-agnostic fingerprints for attribution. Across extensive experiments, our approach achieves AUROC scores exceeding 99% in most scenarios, evaluated on augmented benchmark datasets that pair real speech with synthetic audio generated by multiple synthesis systems. In addition, our robustness analysis underscores the method's ability to maintain high performance even in the presence of moderate additive noise. Due to its simplicity, efficiency, and strong generalization across speech synthesis systems and languages, this technique offers a practical tool for digital forensics and security applications.

Matías Pizarro, Dorothea Kolossa, Asja Fischer

In the past few years, it has been shown that deep learning systems are highly vulnerable under attacks with adversarial examples. Neural-network-based automatic speech recognition (ASR) systems are no exception. Targeted and untargeted attacks can modify an audio input signal in such a way that humans still recognise the same words, while ASR systems are steered to predict a different transcription. In this paper, we propose a defense mechanism against targeted adversarial attacks consisting in removing fast-changing features from the audio signals, either by applying slow feature analysis, a low-pass filter, or both, before feeding the input to the ASR system. We perform an empirical analysis of hybrid ASR models trained on data pre-processed in such a way. While the resulting models perform quite well on benign data, they are significantly more robust against targeted adversarial attacks: Our final, proposed model shows a performance on clean data similar to the baseline model, while being more than four times more robust.