Robustifying automatic speech recognition by extracting slowly varying features

• URL: http://arxiv.org/abs/2112.07400v1
• Date: Tue, 14 Dec 2021 13:50:23 GMT
• Title: Robustifying automatic speech recognition by extracting slowly varying features
• Authors: Matias Pizarro and Dorothea Kolossa and Asja Fischer
• Abstract summary: We propose a defense mechanism against targeted adversarial attacks. We use hybrid ASR models trained on data pre-processed in such a way. Our model shows a performance on clean data similar to the baseline model, while being more than four times more robust.
• Score: 20.96846497286073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Recent improvements of ASR models in the face of adversarial attacks [28.934863462633636]
  Speech Recognition models are vulnerable to adversarial attacks. We show that the relative strengths of different attack algorithms vary considerably when changing the model architecture. We release our source code as a package that should help future research in evaluating their attacks and defenses.
  arXiv  Detail & Related papers  (2022-03-29T22:40:37Z)
• Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition [18.83748866242237]
  We focus on a rigorous and empirical "closed-model adversarial robustness" setting. We propose an advanced Bayesian neural network (BNN) based adversarial detector. We improve detection rate by +2.77 to +5.42% (relative +3.03 to +6.26%) and reduce the word error rate by 5.02 to 7.47% on LibriSpeech datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:17:58Z)
• Language Dependencies in Adversarial Attacks on Speech Recognition Systems [0.0]
  We compare the attackability of a German and an English ASR system. We investigate if one of the language models is more susceptible to manipulations than the other.
  arXiv  Detail & Related papers  (2022-02-01T13:27:40Z)
• Blackbox Untargeted Adversarial Testing of Automatic Speech Recognition Systems [1.599072005190786]
  Speech recognition systems are prevalent in applications for voice navigation and voice control of domestic appliances. Deep neural networks (DNNs) have been shown to be susceptible to adversarial perturbations. To help test the correctness of ASRS, we propose techniques that automatically generate blackbox.
  arXiv  Detail & Related papers  (2021-12-03T10:21:47Z)
• Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models [57.20432226304683]
  Non-autoregressive (NAR) modeling has gained more and more attention in speech processing. We propose a novel end-to-end streaming NAR speech recognition system. We show that the proposed method improves online ASR recognition in low latency conditions.
  arXiv  Detail & Related papers  (2021-07-20T11:42:26Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification [78.51092318750102]
  This work proposes to defend ASV systems against adversarial attacks with a separate detection network. A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples.
  arXiv  Detail & Related papers  (2020-06-11T04:31:56Z)
• Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning [71.17774313301753]
  We explore the robustness of self-supervised learned high-level representations by using them in the defense against adversarial attacks. Experimental results on the ASVspoof 2019 dataset demonstrate that high-level representations extracted by Mockingjay can prevent the transferability of adversarial examples.
  arXiv  Detail & Related papers  (2020-06-05T03:03:06Z)
• Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification [21.582072216282725]
  Machine learning systems and, specifically, automatic speech recognition (ASR) systems are vulnerable to adversarial attacks. In this paper, we focus on hybrid ASR systems and compare four acoustic models regarding their ability to indicate uncertainty under attack. We are able to detect adversarial examples with an area under the receiving operator curve score of more than 0.99.
  arXiv  Detail & Related papers  (2020-05-24T19:31:02Z)
• Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement [102.48582597586233]
  We present a U-Net based attention model, U-Net$_At$, to enhance adversarial speech signals. We conduct experiments on the automatic speech recognition (ASR) task with adversarial audio attacks.
  arXiv  Detail & Related papers  (2020-03-31T02:16:34Z)
• Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition [56.844587127848854]
  We demonstrate that the state-of-the-art gait recognition model is vulnerable to such attacks. We employ a generative adversarial network based architecture to semantically generate adversarial high-quality gait silhouettes or video frames. The experimental results show that if only one-fortieth of the frames are attacked, the accuracy of the target model drops dramatically.
  arXiv  Detail & Related papers  (2020-02-22T10:08:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.