Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples

• URL: http://arxiv.org/abs/2107.00561v1
• Date: Thu, 1 Jul 2021 16:00:09 GMT
• Title: Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples
• Authors: Nelson Manohar-Alers, Ryan Feng, Sahib Singh, Jiguo Song, Atul Prakash
• Abstract summary: We present DeClaW, a system for detecting, classifying, and warning of adversarial inputs presented to a classification neural network. Preliminary findings suggest that AFVs can help distinguish among several types of adversarial attacks with close to 93% accuracy on the CIFAR-10 dataset.
• Score: 4.096598295525345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DeClaW, a system for detecting, classifying, and warning of adversarial inputs presented to a classification neural network. In contrast to current state-of-the-art methods that, given an input, detect whether an input is clean or adversarial, we aim to also identify the types of adversarial attack (e.g., PGD, Carlini-Wagner or clean). To achieve this, we extract statistical profiles, which we term as anomaly feature vectors, from a set of latent features. Preliminary findings suggest that AFVs can help distinguish among several types of adversarial attacks (e.g., PGD versus Carlini-Wagner) with close to 93% accuracy on the CIFAR-10 dataset. The results open the door to using AFV-based methods for exploring not only adversarial attack detection but also classification of the attack type and then design of attack-specific mitigation strategies.

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)
• PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis [2.5347892611213614]
  Deep neural networks for classification are vulnerable to adversarial attacks, where small perturbations to input samples lead to incorrect predictions. We develop a practical method for this characteristic of model prediction and feature attribution to detect adversarial samples. Our approach demonstrates competitive performance even when an adversary is aware of the defense mechanism.
  arXiv  Detail & Related papers  (2024-04-12T21:22:21Z)
• 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)
• 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)
• Detection of Adversarial Supports in Few-shot Classifiers Using Feature Preserving Autoencoders and Self-Similarity [89.26308254637702]
  We propose a detection strategy to highlight adversarial support sets. We make use of feature preserving autoencoder filtering and also the concept of self-similarity of a support set to perform this detection. Our method is attack-agnostic and also the first to explore detection for few-shot classifiers to the best of our knowledge.
  arXiv  Detail & Related papers  (2020-12-09T14:13:41Z)
• Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection [50.03939695025513]
  We propose a new probabilistic adversarial detector motivated by a recently introduced non-robust feature. In this paper, we consider the non-robust features as a common property of adversarial examples, and we deduce it is possible to find a cluster in representation space corresponding to the property. This idea leads us to probability estimate distribution of adversarial representations in a separate cluster, and leverage the distribution for a likelihood based adversarial detector.
  arXiv  Detail & Related papers  (2020-12-07T07:21:18Z)
• 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)
• Adversarial Detection and Correction by Matching Prediction Distributions [0.0]
  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.
  arXiv  Detail & Related papers  (2020-02-21T15:45:42Z)

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.