Results 11 to 20 of about 505,656 (201)
Distributionally Adversarial Attack
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks.
Zheng, Tianhang +2 more
core +5 more sources
A Hybrid Adversarial Attack for Different Application Scenarios
Applied Sciences, 2020 Adversarial attack against natural language has been a hot topic in the field of artificial intelligence security in recent years. It is mainly to study the methods and implementation of generating adversarial examples. The purpose is to better deal with
Xiaohu Du +6 more
doaj +2 more sources
CoRR, 2022 Adversarial attacks have only focused on changing the predictions of the classifier, but their danger greatly depends on how the class is mistaken. For example, when an automatic driving system mistakes a Persian cat for a Siamese cat, it is hardly a ...
Yamasaki, Toshihiko +2 more
core +3 more sources
Review of Artificial Intelligence Adversarial Attack and Defense Technologies
Applied Sciences, 2019 In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields.
Shilin Qiu +3 more
doaj +3 more sources
Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition. [PDF]
Sensors (Basel), 2021 Lal S +7 more
europepmc +2 more sources
Causality adversarial attack generation algorithm for intelligent unmanned communication system [PDF]
Tongxin xuebaoA causality adversarial attack generation algorithm was proposed in response to the causality issue of gradient-based adversarial attack generation algorithms in practical communication system.The sequential input-output features and temporal memory ...
Shuwen YU, Wei XU, Jiacheng YAO
doaj +4 more sources
Multi-target Category Adversarial Example Generating Algorithm Based on GAN [PDF]
Jisuanji kexue, 2022 Although deep neural networks perform well in many areas,research shows that deep neural networks are vulnerable to attacks from adversarial examples.There are many algorithms for attacking neural networks,but the attack speed of most attack algorithms ...
LI Jian, GUO Yan-ming, YU Tian-yuan, WU Yu-lun, WANG Xiang-han, LAO Song-yang
doaj +1 more source
Optical Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 ICCV Workshop ...
Abhiram Gnanasambandam +2 more
openaire +3 more sources
EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT
Heliyon, 2023 The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li +4 more
doaj +1 more source
On the Reversibility of Adversarial Attacks
2021 IEEE International Conference on Image Processing (ICIP), 2021 Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of deep neural network classifiers. In this paper, we investigate the predictability of the mapping between the classes predicted for original images and for their corresponding
Chau Yi Li +4 more
openaire +4 more sources