Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models

• URL: http://arxiv.org/abs/2103.15897v1
• Date: Mon, 29 Mar 2021 19:07:55 GMT
• Title: Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models
• Authors: Josh Kalin, David Noever, Matthew Ciolino, Dominick Hambrick, Gerry Dozier
• Abstract summary: We evaluate the use of multi-spectral image arrays and ensemble learners to combat adversarial attacks. In rough analogy to defending cyber-networks, we combine techniques from both offensive ("red team) and defensive ("blue team") approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image classification is a common step in image recognition for machine learning in overhead applications. When applying popular model architectures like MobileNetV2, known vulnerabilities expose the model to counter-attacks, either mislabeling a known class or altering box location. This work proposes an automated approach to defend these models. We evaluate the use of multi-spectral image arrays and ensemble learners to combat adversarial attacks. The original contribution demonstrates the attack, proposes a remedy, and automates some key outcomes for protecting the model's predictions against adversaries. In rough analogy to defending cyber-networks, we combine techniques from both offensive ("red team") and defensive ("blue team") approaches, thus generating a hybrid protective outcome ("green team"). For machine learning, we demonstrate these methods with 3-color channels plus infrared for vehicles. The outcome uncovers vulnerabilities and corrects them with supplemental data inputs commonly found in overhead cases particularly.

Related papers

• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• Can Adversarial Examples Be Parsed to Reveal Victim Model Information? [62.814751479749695]
  In this work, we ask whether it is possible to infer data-agnostic victim model (VM) information from data-specific adversarial instances. We collect a dataset of adversarial attacks across 7 attack types generated from 135 victim models. We show that a simple, supervised model parsing network (MPN) is able to infer VM attributes from unseen adversarial attacks.
  arXiv  Detail & Related papers  (2023-03-13T21:21:49Z)
• An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks [0.0]
  The vulnerability of state-of-the-art Neural Networks to adversarial input samples has increased drastically. This article proposes a defense algorithm that utilizes the combination of an auto-encoder and block-switching architecture.
  arXiv  Detail & Related papers  (2022-03-11T10:58:24Z)
• 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)
• An Empirical Review of Adversarial Defenses [0.913755431537592]
  Deep neural networks, which form the basis of such systems, are highly susceptible to a specific type of attack, called adversarial attacks. A hacker can, even with bare minimum computation, generate adversarial examples (images or data points that belong to another class, but consistently fool the model to get misclassified as genuine) and crumble the basis of such algorithms. We show two effective techniques, namely Dropout and Denoising Autoencoders, and show their success in preventing such attacks from fooling the model.
  arXiv  Detail & Related papers  (2020-12-10T09:34:41Z)
• Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack [35.0689225703137]
  A machine learning-based security detection model is susceptible to adversarial evasion attacks. We propose an approach called Omni to explore methods that create an ensemble of "unexpected models" In studies with five types of adversarial evasion attacks, we show Omni is a promising approach as a defense strategy.
  arXiv  Detail & Related papers  (2020-11-23T20:02:40Z)
• Online Alternate Generator against Adversarial Attacks [144.45529828523408]
  Deep learning models are notoriously sensitive to adversarial examples which are synthesized by adding quasi-perceptible noises on real images. We propose a portable defense method, online alternate generator, which does not need to access or modify the parameters of the target networks. The proposed method works by online synthesizing another image from scratch for an input image, instead of removing or destroying adversarial noises.
  arXiv  Detail & Related papers  (2020-09-17T07:11:16Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)
• Using an ensemble color space model to tackle adversarial examples [22.732023268348787]
  We propose a 3 step method for defending such attacks. First, we denoise the image using statistical methods. Second, we show that adopting multiple color spaces in the same model can help us to fight these adversarial attacks further.
  arXiv  Detail & Related papers  (2020-03-10T21:20:53Z)
• Deflecting Adversarial Attacks [94.85315681223702]
  We present a new approach towards ending this cycle where we "deflect" adversarial attacks by causing the attacker to produce an input that resembles the attack's target class. We first propose a stronger defense based on Capsule Networks that combines three detection mechanisms to achieve state-of-the-art detection performance.
  arXiv  Detail & Related papers  (2020-02-18T06:59:13Z)

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.