Exploring Adversarial Examples via Invertible Neural Networks

• URL: http://arxiv.org/abs/2012.13111v1
• Date: Thu, 24 Dec 2020 05:17:21 GMT
• Title: Exploring Adversarial Examples via Invertible Neural Networks
• Authors: Ruqi Bai and Saurabh Bagchi and David I. Inouye
• Abstract summary: Adversarial examples (AEs) are images that can mislead deep neural network (DNN) classifiers via introducing slight perturbations into original images. This security vulnerability has led to vast research in recent years because it can introduce real-world threats into systems that rely on neural networks. We propose a new way of achieving such understanding through a recent development, namely, invertible neural models with Lipschitz continuous mapping functions from the input to the output.
• Score: 10.320129984220857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial examples (AEs) are images that can mislead deep neural network (DNN) classifiers via introducing slight perturbations into original images. This security vulnerability has led to vast research in recent years because it can introduce real-world threats into systems that rely on neural networks. Yet, a deep understanding of the characteristics of adversarial examples has remained elusive. We propose a new way of achieving such understanding through a recent development, namely, invertible neural models with Lipschitz continuous mapping functions from the input to the output. With the ability to invert any latent representation back to its corresponding input image, we can investigate adversarial examples at a deeper level and disentangle the adversarial example's latent representation. Given this new perspective, we propose a fast latent space adversarial example generation method that could accelerate adversarial training. Moreover, this new perspective could contribute to new ways of adversarial example detection.

Related papers

• A Survey on Transferability of Adversarial Examples across Deep Neural Networks [53.04734042366312]
  adversarial examples can manipulate machine learning models into making erroneous predictions. The transferability of adversarial examples enables black-box attacks which circumvent the need for detailed knowledge of the target model. This survey explores the landscape of the adversarial transferability of adversarial examples.
  arXiv  Detail & Related papers  (2023-10-26T17:45:26Z)
• Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques [1.0718756132502771]
  adversarial examples are subtle perturbations artfully injected into clean images or videos. Deepfakes have emerged as a potent tool to manipulate public opinion and tarnish the reputations of public figures. This article delves into the multifaceted world of adversarial examples, elucidating the underlying principles behind their capacity to deceive deep learning algorithms.
  arXiv  Detail & Related papers  (2023-02-22T23:48:19Z)
• Searching for the Essence of Adversarial Perturbations [73.96215665913797]
  We show that adversarial perturbations contain human-recognizable information, which is the key conspirator responsible for a neural network's erroneous prediction. This concept of human-recognizable information allows us to explain key features related to adversarial perturbations.
  arXiv  Detail & Related papers  (2022-05-30T18:04:57Z)
• Collaborative Adversarial Training [82.25340762659991]
  We show that some collaborative examples, nearly perceptually indistinguishable from both adversarial and benign examples, can be utilized to enhance adversarial training. A novel method called collaborative adversarial training (CoAT) is thus proposed to achieve new state-of-the-arts.
  arXiv  Detail & Related papers  (2022-05-23T09:41:41Z)
• Adversarial Attack via Dual-Stage Network Erosion [7.28871533402894]
  Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. This paper proposes to improve the transferability of adversarial examples, and applies dual-stage feature-level perturbations to an existing model to implicitly create a set of diverse models. We conduct comprehensive experiments both on non-residual and residual networks, and obtain more transferable adversarial examples with the computational cost similar to the state-of-the-art method.
  arXiv  Detail & Related papers  (2022-01-01T02:38:09Z)
• Adversarial Examples Detection with Bayesian Neural Network [57.185482121807716]
  We propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors. We propose a novel Bayesian adversarial example detector, short for BATer, to improve the performance of adversarial example detection.
  arXiv  Detail & Related papers  (2021-05-18T15:51:24Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting [71.57324258813674]
  convolutional neural networks (CNNs) have proved to reach super-human performance in visual recognition tasks. CNNs can easily be fooled by adversarial examples, i.e., maliciously-crafted images that force the networks to predict an incorrect output. This paper extensively explores the detection of adversarial examples via image transformations and proposes a novel methodology.
  arXiv  Detail & Related papers  (2021-01-27T14:50:41Z)
• Adversarial Examples on Object Recognition: A Comprehensive Survey [1.976652238476722]
  Deep neural networks are at the forefront of machine learning research. adversarial examples are intentionally designed to test the network's sensitivity to distribution drifts. We discuss the impact of adversarial examples on security, safety, and robustness of neural networks.
  arXiv  Detail & Related papers  (2020-08-07T08:51:21Z)

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.