Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components

• URL: http://arxiv.org/abs/2007.07001v3
• Date: Wed, 13 Jan 2021 16:28:22 GMT
• Title: Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components
• Authors: Ken Alparslan, Yigit Alparslan, Matthew Burlick
• Abstract summary: Speech-to-text neural networks that are widely used today are prone to misclassify adversarial attacks. We craft adversarial wave forms via Connectionist Temporal Classification Loss Function, and attack DeepSpeech, a speech-to-text neural network implemented by Mozilla. We achieve 100% adversarial success rate (zero successful classification by DeepSpeech) on all 25 adversarial wave forms that we crafted.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Adversarial attacks are inputs that are similar to original inputs but altered on purpose. Speech-to-text neural networks that are widely used today are prone to misclassify adversarial attacks. In this study, first, we investigate the presence of targeted adversarial attacks by altering wave forms from Common Voice data set. We craft adversarial wave forms via Connectionist Temporal Classification Loss Function, and attack DeepSpeech, a speech-to-text neural network implemented by Mozilla. We achieve 100% adversarial success rate (zero successful classification by DeepSpeech) on all 25 adversarial wave forms that we crafted. Second, we investigate the use of PCA as a defense mechanism against adversarial attacks. We reduce dimensionality by applying PCA to these 25 attacks that we created and test them against DeepSpeech. We observe zero successful classification by DeepSpeech, which suggests PCA is not a good defense mechanism in audio domain. Finally, instead of using PCA as a defense mechanism, we use PCA this time to craft adversarial inputs under a black-box setting with minimal adversarial knowledge. With no knowledge regarding the model, parameters, or weights, we craft adversarial attacks by applying PCA to samples from Common Voice data set and achieve 100% adversarial success under black-box setting again when tested against DeepSpeech. We also experiment with different percentage of components necessary to result in a classification during attacking process. In all cases, adversary becomes successful.

Related papers

• AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
  Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries. We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots. We also show that ACPT is robust to 3 types of adaptive attacks.
  arXiv  Detail & Related papers  (2024-08-04T09:53:50Z)
• Preserving Semantics in Textual Adversarial Attacks [0.0]
  Up to 70% of adversarial examples generated by adversarial attacks should be discarded because they do not preserve semantics. We propose a new, fully supervised sentence embedding technique called Semantics-Preserving-Encoder (SPE) Our method outperforms existing sentence encoders used in adversarial attacks by achieving 1.2x - 5.1x better real attack success rate.
  arXiv  Detail & Related papers  (2022-11-08T12:40:07Z)
• Versatile Weight Attack via Flipping Limited Bits [68.45224286690932]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage. Considering the effectiveness and stealthiness goals, we provide a general formulation to perform the bit-flip based weight attack. We present two cases of the general formulation with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA)
  arXiv  Detail & Related papers  (2022-07-25T03:24:58Z)
• Dictionary Attacks on Speaker Verification [15.00667613025837]
  We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial optimization to maximize raw similarity of speaker embeddings between a seed speech sample and a proxy population. We show that, combined with multiple attempts, this attack opens even more to serious issues on the security of these systems.
  arXiv  Detail & Related papers  (2022-04-24T15:31:41Z)
• Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems [33.21836814000979]
  This paper explores speech specific defenses using the mel spectral domain, and introduces a novel defense method called'mel domain noise flooding' (MDNF) MDNF applies additive noise to the mel spectrogram of a speech utterance prior to re-synthesising the audio signal. We test the defenses against strong white-box adversarial attacks such as projected gradient descent (PGD) and Carlini-Wagner (CW) attacks.
  arXiv  Detail & Related papers  (2022-03-29T06:58:26Z)
• Zero-Query Transfer Attacks on Context-Aware Object Detectors [95.18656036716972]
  Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check.
  arXiv  Detail & Related papers  (2022-03-29T04:33:06Z)
• Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning [95.60856995067083]
  This work is among the first to perform adversarial defense for ASV without knowing the specific attack algorithms. We propose to perform adversarial defense from two perspectives: 1) adversarial perturbation purification and 2) adversarial perturbation detection. Experimental results show that our detection module effectively shields the ASV by detecting adversarial samples with an accuracy of around 80%.
  arXiv  Detail & Related papers  (2021-06-01T07:10:54Z)
• Cortical Features for Defense Against Adversarial Audio Attacks [55.61885805423492]
  We propose using a computational model of the auditory cortex as a defense against adversarial attacks on audio. We show that the cortical features help defend against universal adversarial examples.
  arXiv  Detail & Related papers  (2021-01-30T21:21:46Z)
• Composite Adversarial Attacks [57.293211764569996]
  Adversarial attack is a technique for deceiving Machine Learning (ML) models. In this paper, a new procedure called Composite Adrial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms. CAA beats 10 top attackers on 11 diverse defenses with less elapsed time.
  arXiv  Detail & Related papers  (2020-12-10T03:21:16Z)
• VenoMave: Targeted Poisoning Against Speech Recognition [30.448709704880518]
  VENOMAVE is the first training-time poisoning attack against speech recognition. We evaluate our attack on two datasets: TIDIGITS and Speech Commands.
  arXiv  Detail & Related papers  (2020-10-21T00:30:08Z)

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.