Research on Generating Adversarial Examples in Applications
نویسندگان
چکیده
منابع مشابه
Generating Natural Adversarial Examples
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, th...
متن کاملGenerating Adversarial Examples with Adversarial Networks
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires mor...
متن کاملCrafting Adversarial Examples For Speech Paralinguistics Applications
Computational paralinguistic analysis is increasingly being used in a wide range of applications, including securitysensitive applications such as speaker verification, deceptive speech detection, and medical diagnostics. While state-ofthe-art machine learning techniques, such as deep neural networks, can provide robust and accurate speech analysis, they are susceptible to adversarial attacks. ...
متن کاملAdversarial Examples Generation and Defense Based on Generative Adversarial Network
We propose a novel generative adversarial network to generate and defend adversarial examples for deep neural networks (DNN). The adversarial stability of a network D is improved by training alternatively with an additional network G. Our experiment is carried out on MNIST, and the adversarial examples are generated in an efficient way compared with wildly-used gradient based methods. After tra...
متن کاملSemantic Adversarial Examples
Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error. Such images, however, contain artificial perturbations that make them somewhat distinguishable from natural images. This property is...
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ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2021
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1757/1/012045