Results 1 to 10 of about 177,286 (215)
Minimum Adversarial Examples [PDF]
Entropy, 2022 Deep neural networks in the area of information security are facing a severe threat from adversarial examples (AEs). Existing methods of AE generation use two optimization models: (1) taking the successful attack as the objective function and limiting ...
Zhenyu Du, Fangzheng Liu, Xuehu Yan
doaj +4 more sources
Smooth adversarial examples [PDF]
EURASIP Journal on Information Security, 2020 This paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts.
Hanwei Zhang +3 more
doaj +4 more sources
Experiments on Adversarial Examples for Deep Learning Model Using Multimodal Sensors [PDF]
Sensors, 2022 Recently, artificial intelligence (AI) based on IoT sensors has been widely used, which has increased the risk of attacks targeting AI. Adversarial examples are among the most serious types of attacks in which the attacker designs inputs that can cause ...
Ade Kurniawan +2 more
doaj +2 more sources
Clustering Approach for Detecting Multiple Types of Adversarial Examples [PDF]
Sensors, 2022 With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi +3 more
doaj +2 more sources
Targeted Universal Adversarial Examples for Remote Sensing
Remote Sensing, 2022 Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples.
Tao Bai, Hao Wang, Bihan Wen
doaj +3 more sources
Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks
IEEE Access, 2020 Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree ...
Xueqin Zhang +4 more
doaj +3 more sources
Adversarial Examples Are Not Real Features [PDF]
Advances in Neural Information Processing Systems 36, 2023 The existence of adversarial examples has been a mystery for years and attracted much interest. A well-known theory by \citet{ilyas2019adversarial} explains adversarial vulnerability from a data perspective by showing that one can extract non-robust features from adversarial examples and these features alone are useful for classification.
Ang Li +3 more
openalex +4 more sources
Defending against and generating adversarial examples together with generative adversarial networks [PDF]
Scientific ReportsAlthough deep neural networks have achieved great success in many tasks, they encounter security threats and are often fooled by adversarial examples, which are created by making slight modifications to pixel values. To address these problems, a novel DG-
Ying Wang, Xiao Liao, Wei Cui, Yang Yang
doaj +2 more sources
Understanding adversarial robustness against on-manifold adversarial examples
Pattern RecognitionDeep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-trained model can be easily attacked by adding small perturbations to the original data. One of the hypotheses of the existence of the adversarial examples is the off-manifold assumption: adversarial examples lie off the data manifold. However, recent research showed
Yanbo Fan, Zhi-Quan Luo
exaly +3 more sources
Adversarial Examples Detection Method Based on Image Denoising and Compression [PDF]
Jisuanji gongcheng, 2023 Numerous deep learning achievements in the field of computer vision have been widely applied in real life. However, adversarial examples can lead to false positives in deep learning models with high confidence, resulting in serious security consequences.
Feiyu WANG, Fan ZHANG, Jiayu DU, Hongle LEI, Xiaofeng QI
doaj +1 more source