Time-Frequency Jointed Imperceptible Adversarial Attack to Brainprint Recognition with Deep Learning Models

• URL: http://arxiv.org/abs/2403.10021v3
• Date: Mon, 1 Jul 2024 03:37:51 GMT
• Title: Time-Frequency Jointed Imperceptible Adversarial Attack to Brainprint Recognition with Deep Learning Models
• Authors: Hangjie Yi, Yuhang Ming, Dongjun Liu, Wanzeng Kong,
• Abstract summary: We introduce a novel adversarial attack method that jointly attacks time-domain and frequency-domain EEG signals. Our method achieves state-of-the-art attack performance on three datasets and three deep-learning models.
• Score: 7.019918611093632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EEG-based brainprint recognition with deep learning models has garnered much attention in biometric identification. Yet, studies have indicated vulnerability to adversarial attacks in deep learning models with EEG inputs. In this paper, we introduce a novel adversarial attack method that jointly attacks time-domain and frequency-domain EEG signals by employing wavelet transform. Different from most existing methods which only target time-domain EEG signals, our method not only takes advantage of the time-domain attack's potent adversarial strength but also benefits from the imperceptibility inherent in frequency-domain attack, achieving a better balance between attack performance and imperceptibility. Extensive experiments are conducted in both white- and grey-box scenarios and the results demonstrate that our attack method achieves state-of-the-art attack performance on three datasets and three deep-learning models. In the meanwhile, the perturbations in the signals attacked by our method are barely perceptible to the human visual system.

Related papers

• Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost [5.197034517903854]
  We investigate a new test-time adversarial defense method via diffusion-based recovery along opposite adversarial paths (OAPs) We present a purifier that can be plugged into a pre-trained model to resist adversarial attacks.
  arXiv  Detail & Related papers  (2024-10-22T08:32:17Z)
• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics [48.683457383784145]
  Three approaches to presentation attack detection based on r: (i) the physiological domain, (ii) the Deepfakes domain, and (iii) a new Presentation Attack domain. Results show a 21.70% decrease in average classification error rate (ACER) when the presentation attack domain is compared to the physiological and Deepfakes domains. Experiments highlight the efficiency of transfer learning in r-based models and perform well in presentation attack detection in instruments that do not allow copying of this physiological feature.
  arXiv  Detail & Related papers  (2023-10-03T15:24:15Z)
• Investigating Human-Identifiable Features Hidden in Adversarial Perturbations [54.39726653562144]
  Our study explores up to five attack algorithms across three datasets. We identify human-identifiable features in adversarial perturbations. Using pixel-level annotations, we extract such features and demonstrate their ability to compromise target models.
  arXiv  Detail & Related papers  (2023-09-28T22:31:29Z)
• Exploring the Physical World Adversarial Robustness of Vehicle Detection [13.588120545886229]
  Adrial attacks can compromise the robustness of real-world detection models. We propose an innovative instant-level data generation pipeline using the CARLA simulator. Our findings highlight diverse model performances under adversarial conditions.
  arXiv  Detail & Related papers  (2023-08-07T11:09:12Z)
• Adversarial Vulnerability of Temporal Feature Networks for Object Detection [5.525433572437716]
  We study whether temporal feature networks for object detection are vulnerable to universal adversarial attacks. We evaluate attacks of two types: imperceptible noise for the whole image and locally-bound adversarial patch. Our experiments on KITTI and nuScenes datasets demonstrate, that a model robustified via K-PGD is able to withstand the studied attacks.
  arXiv  Detail & Related papers  (2022-08-23T07:08:54Z)
• Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems [50.758281304737444]
  We propose to explore adversarial examples and attack detection on reinforcement learning-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data.
  arXiv  Detail & Related papers  (2021-12-02T04:12:24Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Detection Defense Against Adversarial Attacks with Saliency Map [7.736844355705379]
  It is well established that neural networks are vulnerable to adversarial examples, which are almost imperceptible on human vision. Existing defenses are trend to harden the robustness of models against adversarial attacks. We propose a novel method combined with additional noises and utilize the inconsistency strategy to detect adversarial examples.
  arXiv  Detail & Related papers  (2020-09-06T13:57:17Z)
• 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.