Adversarial Detection and Correction by Matching Prediction Distributions

• URL: http://arxiv.org/abs/2002.09364v1
• Date: Fri, 21 Feb 2020 15:45:42 GMT
• Title: Adversarial Detection and Correction by Matching Prediction Distributions
• Authors: Giovanni Vacanti and Arnaud Van Looveren
• Abstract summary: The detector almost completely neutralises powerful attacks like Carlini-Wagner or SLIDE on MNIST and Fashion-MNIST. We show that our method is still able to detect the adversarial examples in the case of a white-box attack where the attacker has full knowledge of both the model and the defence.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel adversarial detection and correction method for machine learning classifiers.The detector consists of an autoencoder trained with a custom loss function based on the Kullback-Leibler divergence between the classifier predictions on the original and reconstructed instances.The method is unsupervised, easy to train and does not require any knowledge about the underlying attack. The detector almost completely neutralises powerful attacks like Carlini-Wagner or SLIDE on MNIST and Fashion-MNIST, and remains very effective on CIFAR-10 when the attack is granted full access to the classification model but not the defence. We show that our method is still able to detect the adversarial examples in the case of a white-box attack where the attacker has full knowledge of both the model and the defence and investigate the robustness of the attack. The method is very flexible and can also be used to detect common data corruptions and perturbations which negatively impact the model performance. We illustrate this capability on the CIFAR-10-C dataset.

Related papers

• An Adversarial Approach to Evaluating the Robustness of Event Identification Models [12.862865254507179]
  This paper considers a physics-based modal decomposition method to extract features for event classification. The resulting classifiers are tested against an adversarial algorithm to evaluate their robustness.
  arXiv  Detail & Related papers  (2024-02-19T18:11:37Z)
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack [53.032801921915436]
  Human Activity Recognition (HAR) has been employed in a wide range of applications, e.g. self-driving cars. Recently, the robustness of skeleton-based HAR methods have been questioned due to their vulnerability to adversarial attacks. We show such threats exist, even when the attacker only has access to the input/output of the model. We propose the very first black-box adversarial attack approach in skeleton-based HAR called BASAR.
  arXiv  Detail & Related papers  (2022-11-21T09:51:28Z)
• Detection and Mitigation of Byzantine Attacks in Distributed Training [24.951227624475443]
  An abnormal Byzantine behavior of the worker nodes can derail the training and compromise the quality of the inference. Recent work considers a wide range of attack models and has explored robust aggregation and/or computational redundancy to correct the distorted gradients. In this work, we consider attack models ranging from strong ones: $q$ omniscient adversaries with full knowledge of the defense protocol that can change from iteration to iteration to weak ones: $q$ randomly chosen adversaries with limited collusion abilities.
  arXiv  Detail & Related papers  (2022-08-17T05:49:52Z)
• On Trace of PGD-Like Adversarial Attacks [77.75152218980605]
  Adversarial attacks pose safety and security concerns for deep learning applications. We construct Adrial Response Characteristics (ARC) features to reflect the model's gradient consistency. Our method is intuitive, light-weighted, non-intrusive, and data-undemanding.
  arXiv  Detail & Related papers  (2022-05-19T14:26:50Z)
• Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples [107.38834819682315]
  We study a conceptually simple approach to defend few-shot classifiers against adversarial attacks. We propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering. Our evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance.
  arXiv  Detail & Related papers  (2021-10-24T05:46:03Z)
• Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks [7.150136251781658]
  Poisoning attacks are a category of adversarial machine learning threats. In this paper, we propose CAE, a Classification Auto-Encoder based detector against poisoned data. We show that an enhanced version of CAE (called CAE+) does not have to employ a clean data set to train the defense model.
  arXiv  Detail & Related papers  (2021-08-09T17:46:52Z)
• Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
  CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
  arXiv  Detail & Related papers  (2021-05-31T17:01:05Z)
• Self-Supervised Adversarial Example Detection by Disentangled Representation [16.98476232162835]
  We train an autoencoder, assisted by a discriminator network, over both correctly paired class/semantic features and incorrectly paired class/semantic features to reconstruct benign and counterexamples. This mimics the behavior of adversarial examples and can reduce the unnecessary generalization ability of autoencoder. Compared with the state-of-the-art self-supervised detection methods, our method exhibits better performance in various measurements.
  arXiv  Detail & Related papers  (2021-05-08T12:48:18Z)
• ExAD: An Ensemble Approach for Explanation-based Adversarial Detection [17.455233006559734]
  We propose ExAD, a framework to detect adversarial examples using an ensemble of explanation techniques. We evaluate our approach using six state-of-the-art adversarial attacks on three image datasets.
  arXiv  Detail & Related papers  (2021-03-22T00:53:07Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)

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.