Results 231 to 240 of about 31,109 (263)
Benchmarking Adversarial Patch Against Aerial Detection
IEEE Transactions on Geoscience and Remote Sensing, 2022 DNNs are vulnerable to adversarial examples, which poses great security concerns for security-critical systems. In this paper, a novel adaptive-patch-based physical attack (AP-PA) framework is proposed, which aims to generate adversarial patches that are adaptive in both physical dynamics and varying scales, and by which the particular targets can be ...
Jiawei Lian, Shaohui Mei, Shun Zhang
exaly +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Cross-Shaped Adversarial Patch Attack
IEEE Transactions on Circuits and Systems for Video TechnologyYu Ran, Weijia Wang, Mingjie Li
exaly +2 more sources
A data independent approach to generate adversarial patches
Machine Vision and Applications, 2021Deep neural networks are vulnerable to adversarial examples, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently, adversarial patch, with perturbations confined to a small and localized patch, emerged for its easy accessibility in real-world attack.
Xingyu Zhou 0002 +4 more
openaire +1 more source
Enhancing the Transferability of Adversarial Examples with Random Patch
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022Adversarial examples can fool deep learning models, and their transferability is critical for attacking black-box models in real-world scenarios. Existing state-of-the-art transferable adversarial attacks tend to exploit intrinsic features of objects to generate adversarial examples.
Yaoyuan Zhang +5 more
openaire +1 more source
Naturalistic Physical Adversarial Patch for Object Detectors
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches. This leads to conspicuous and attention-grabbing patterns for the generated patches which can be easily identified by humans. To address this issue, we pro-pose a method to
Hu, Y.-C.-T. +5 more
openaire +2 more sources
2017
Deep learning algorithms have gained a lot of popularity in recent years due to their state-of-the-art results in computer vision applications. Despite their success, studies have shown that neural networks are vulnerable to attacks via perturbations in input images in various forms, called adversarial examples.
openaire +1 more source
Deep learning algorithms have gained a lot of popularity in recent years due to their state-of-the-art results in computer vision applications. Despite their success, studies have shown that neural networks are vulnerable to attacks via perturbations in input images in various forms, called adversarial examples.
openaire +1 more source
Scaling Resilient Adversarial Patch
2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2021Yunhong Yin +4 more
openaire +1 more source
Visually imperceptible adversarial patch attacks
Computers & Security, 2022Yaguan Qian +6 more
openaire +1 more source
Universal Adversarial Patch Attack for Automatic Checkout Using Perceptual and Attentional Bias
IEEE Transactions on Image Processing, 2022Jiakai Wang, Aishan Liu, Xiao Bai
exaly