Results 151 to 160 of about 852,288 (193)
Adversarial Attacks and Defenses
SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, 2021 Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Mengnan Du, Ruocheng Guo, Ninghao Liu
exaly +4 more sources
Open-Set Adversarial Defense [PDF]
Lecture Notes in Computer Science, 2020 Accepted by ECCV ...
Vishal M. Patel, Pong C Yuen, Rui Shao
exaly +4 more sources
Open-Set Adversarial Defense With Clean-Adversarial Mutual Learning [PDF]
International Journal of Computer Vision, 2022 Accepted by International Journal of Computer Vision (IJCV) 2022. Code will be available at https://github.com/rshaojimmy/ECCV2020-OSAD.
Vishal M. Patel, Pong C Yuen, Rui Shao
exaly +5 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Adversarial Attacks and Defenses on Graphs
ACM SIGKDD Explorations Newsletter, 2021Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
Wei Jin 0009 +6 more
openaire +1 more source
Sinkhorn Adversarial Attack and Defense
IEEE Transactions on Image Processing, 2022Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance.
openaire +2 more sources
Robust Defense Against Adversarial Attacks with Defensive Preprocessing and Adversarial Training
2025 IEEE International Conference on Consumer Electronics (ICCE)Deep learning technologies have rapidly advanced, but concerns about their security and vulnerability to threats have emerged. Adversarial attacks, using carefully crafted perturbations, exploit these weaknesses, posing serious risks. This study introduces an integrated defensive preprocessing and adversarial training pipeline as a robust defense ...
Chih-Yang Lin +4 more
openaire +1 more source