Results 11 to 20 of about 1,236,597 (154)
Adversarial Attack with Raindrops [PDF]
CoRR, 2023 Deep neural networks (DNNs) are known to be vulnerable to adversarial examples, which are usually designed artificially to fool DNNs, but rarely exist in real-world scenarios.
Jiyuan Liu +4 more
semanticscholar +3 more sources
Optical Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We introduce OPtical ADversarial attack (OPAD). OPAD is an adversarial attack in the physical space aiming to fool image classifiers without physically touching the objects (e.g., moving or painting the objects).
Abhiram Gnanasambandam +2 more
semanticscholar +4 more sources
Adversarial Attack and Defense: A Survey
Electronics (Switzerland), 2022 In recent years, artificial intelligence technology represented by deep learning has achieved remarkable results in image recognition, semantic analysis, natural language processing and other fields.
Erlu He, Yangyang Zhao
exaly +2 more sources
Meta Gradient Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 In recent years, research on adversarial attacks has be-come a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a ...
Zheng Yuan +5 more
semanticscholar +4 more sources
Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network
Remote Sensing, 2021 Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems.
Chuan Du
exaly +3 more sources
Review of Artificial Intelligence Adversarial Attack and Defense Technologies
Applied Sciences (Switzerland), 2019 In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields.
Qihe Liu, Shilin Qiu, Liu Qihe
exaly +3 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
Frequency Domain Model Augmentation for Adversarial Attack [PDF]
European Conference on Computer Vision, 2022 . For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that the transferability of adversarial examples can be improved by attacking ...
Yuyang Long +6 more
semanticscholar +1 more source
Towards Adversarial Attack on Vision-Language Pre-training Models [PDF]
ACM Multimedia, 2022 While vision-language pre-training model (VLP) has shown revolutionary improvements on various vision-language (V+L) tasks, the studies regarding its adversarial robustness remain largely unexplored.
Jiaming Zhang, Qiaomin Yi, Jitao Sang
semanticscholar +1 more source
Content-based Unrestricted Adversarial Attack [PDF]
Neural Information Processing Systems, 2023 Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception and deep ...
Zhaoyu Chen +5 more
semanticscholar +1 more source