Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN

• URL: http://arxiv.org/abs/2001.09993v1
• Date: Mon, 27 Jan 2020 07:32:46 GMT
• Title: Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
• Authors: Jean-Christophe Burnel (OBELIX), Kilian Fatras (OBELIX), Nicolas Courty (OBELIX)
• Abstract summary: We present a new method which is able to generate natural adversarial examples from the true data following the second paradigm. We provide a proof of concept of our method by generating adversarial hyperspectral signatures on a remote sensing dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial examples are a hot topic due to their abilities to fool a classifier's prediction. There are two strategies to create such examples, one uses the attacked classifier's gradients, while the other only requires access to the clas-sifier's prediction. This is particularly appealing when the classifier is not full known (black box model). In this paper, we present a new method which is able to generate natural adversarial examples from the true data following the second paradigm. Based on Generative Adversarial Networks (GANs) [5], it reweights the true data empirical distribution to encourage the classifier to generate ad-versarial examples. We provide a proof of concept of our method by generating adversarial hyperspectral signatures on a remote sensing dataset.

Related papers

• Wide Two-Layer Networks can Learn from Adversarial Perturbations [27.368408524000778]
  We theoretically explain the counterintuitive success of perturbation learning. We prove that adversarial perturbations contain sufficient class-specific features for networks to generalize from them.
  arXiv  Detail & Related papers  (2024-10-31T06:55:57Z)
• Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation [52.72682366640554]
  Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author or by someone else. It has been shown that many AV systems are vulnerable to adversarial attacks, where a malicious author actively tries to fool the classifier by either concealing their writing style, or by imitating the style of another author.
  arXiv  Detail & Related papers  (2024-03-17T16:36:26Z)
• NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries? [61.58261351116679]
  We introduce a two-stage adversarial example generation framework (NaturalAdversaries) for natural language understanding tasks. It is adaptable to both black-box and white-box adversarial attacks based on the level of access to the model parameters. Our results indicate these adversaries generalize across domains, and offer insights for future research on improving robustness of neural text classification models.
  arXiv  Detail & Related papers  (2022-11-08T16:37:34Z)
• Unrestricted Adversarial Samples Based on Non-semantic Feature Clusters Substitution [1.8782750537161608]
  We introduce "unrestricted" perturbations that create adversarial samples by using spurious relations learned by model training. Specifically, we find feature clusters in non-semantic features that are strongly correlated with model judgment results. We create adversarial samples by using them to replace the corresponding feature clusters in the target image.
  arXiv  Detail & Related papers  (2022-08-31T07:42:36Z)
• On the Transferability of Adversarial Attacksagainst Neural Text Classifier [121.6758865857686]
  We investigate the transferability of adversarial examples for text classification models. We propose a genetic algorithm to find an ensemble of models that can induce adversarial examples to fool almost all existing models. We derive word replacement rules that can be used for model diagnostics from these adversarial examples.
  arXiv  Detail & Related papers  (2020-11-17T10:45:05Z)
• Toward Scalable and Unified Example-based Explanation and Outlier Detection [128.23117182137418]
  We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
  arXiv  Detail & Related papers  (2020-11-11T05:58:17Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)
• Second-Order NLP Adversarial Examples [0.18855270809505867]
  Adrial example generation methods rely on models like language models or sentence encoders to determine if potential adversarial examples are valid. In these methods, a valid adversarial example fools the model being attacked, and is determined to be semantically or syntactically valid by a second model. We contend that these adversarial examples may not be flaws in the attacked model, but flaws in the model that determines validity.
  arXiv  Detail & Related papers  (2020-10-05T04:32:38Z)
• ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples [4.509133544449485]
  Adrial examples are inputs with special perturbations ignored by human eyes. ManiGen generates adversarial examples by searching along the manifold. ManiGen can more effectively attack classifiers with state-of-the-art defenses.
  arXiv  Detail & Related papers  (2020-07-11T17:34:17Z)
• Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations [65.05561023880351]
  Adversarial examples are malicious inputs crafted to induce misclassification. This paper studies a complementary failure mode, invariance-based adversarial examples. We show that defenses against sensitivity-based attacks actively harm a model's accuracy on invariance-based attacks.
  arXiv  Detail & Related papers  (2020-02-11T18:50:23Z)

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.