Exploiting Vulnerabilities in Speech Translation Systems through Targeted Adversarial Attacks

• URL: http://arxiv.org/abs/2503.00957v2
• Date: Wed, 05 Mar 2025 03:07:49 GMT
• Title: Exploiting Vulnerabilities in Speech Translation Systems through Targeted Adversarial Attacks
• Authors: Chang Liu, Haolin Wu, Xi Yang, Kui Zhang, Cong Wu, Weiming Zhang, Nenghai Yu, Tianwei Zhang, Qing Guo, Jie Zhang,
• Abstract summary: This paper explores methods of compromising speech translation systems through imperceptible audio manipulations.<n>We present two innovative approaches: (1) the injection of perturbation into source audio, and (2) the generation of adversarial music designed to guide targeted translation.<n>Our experiments reveal that carefully crafted audio perturbations can mislead translation models to produce targeted, harmful outputs, while adversarial music achieve this goal more covertly.<n>The implications of this research extend beyond immediate security concerns, shedding light on the interpretability and robustness of neural speech processing systems.
• Score: 59.87470192277124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As speech translation (ST) systems become increasingly prevalent, understanding their vulnerabilities is crucial for ensuring robust and reliable communication. However, limited work has explored this issue in depth. This paper explores methods of compromising these systems through imperceptible audio manipulations. Specifically, we present two innovative approaches: (1) the injection of perturbation into source audio, and (2) the generation of adversarial music designed to guide targeted translation, while also conducting more practical over-the-air attacks in the physical world. Our experiments reveal that carefully crafted audio perturbations can mislead translation models to produce targeted, harmful outputs, while adversarial music achieve this goal more covertly, exploiting the natural imperceptibility of music. These attacks prove effective across multiple languages and translation models, highlighting a systemic vulnerability in current ST architectures. The implications of this research extend beyond immediate security concerns, shedding light on the interpretability and robustness of neural speech processing systems. Our findings underscore the need for advanced defense mechanisms and more resilient architectures in the realm of audio systems. More details and samples can be found at https://adv-st.github.io.

Related papers

• Measuring the Robustness of Audio Deepfake Detectors [59.09338266364506]
  This work systematically evaluates the robustness of 10 audio deepfake detection models against 16 common corruptions. Using both traditional deep learning models and state-of-the-art foundation models, we make four unique observations.
  arXiv  Detail & Related papers  (2025-03-21T23:21:17Z)
• "I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models [0.9480364746270077]
  This paper explores audio jailbreaks targeting Audio-Language Models (ALMs)<n>We construct adversarial perturbations that generalize across prompts, tasks, and even base audio samples.<n>We analyze how ALMs interpret these audio adversarial examples and reveal them to encode imperceptible first-person toxic speech.
  arXiv  Detail & Related papers  (2025-02-02T08:36:23Z)
• Can DeepFake Speech be Reliably Detected? [17.10792531439146]
  This work presents the first systematic study of active malicious attacks against state-of-the-art open-source speech detectors. The results highlight the urgent need for more robust detection methods in the face of evolving adversarial threats.
  arXiv  Detail & Related papers  (2024-10-09T06:13:48Z)
• Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark. It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content. It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• Instruct2Attack: Language-Guided Semantic Adversarial Attacks [76.83548867066561]
  Instruct2Attack (I2A) is a language-guided semantic attack that generates meaningful perturbations according to free-form language instructions. We make use of state-of-the-art latent diffusion models, where we adversarially guide the reverse diffusion process to search for an adversarial latent code conditioned on the input image and text instruction. We show that I2A can successfully break state-of-the-art deep neural networks even under strong adversarial defenses.
  arXiv  Detail & Related papers  (2023-11-27T05:35:49Z)
• Red Teaming Language Model Detectors with Language Models [114.36392560711022]
  Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. Recent works have proposed algorithms to detect LLM-generated text and protect LLMs. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
  arXiv  Detail & Related papers  (2023-05-31T10:08:37Z)
• Defense Against Adversarial Attacks on Audio DeepFake Detection [0.4511923587827302]
  Audio DeepFakes (DF) are artificially generated utterances created using deep learning. Multiple neural network-based methods to detect generated speech have been proposed to prevent the threats.
  arXiv  Detail & Related papers  (2022-12-30T08:41:06Z)
• TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation [61.564874831498145]
  TranSpeech is a speech-to-speech translation model with bilateral perturbation. We establish a non-autoregressive S2ST technique, which repeatedly masks and predicts unit choices. TranSpeech shows a significant improvement in inference latency, enabling speedup up to 21.4x than autoregressive technique.
  arXiv  Detail & Related papers  (2022-05-25T06:34:14Z)
• SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems [28.635467696564703]
  We show that the end-to-end architecture of speech and speaker systems makes attacks and defenses against them substantially different than those in the image space. We then demonstrate experimentally that attacks against these models almost universally fail to transfer.
  arXiv  Detail & Related papers  (2020-07-13T18:52:25Z)

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.