Results 21 to 30 of about 85,147 (260)

Adversarial Self-Supervised Learning for Robust SAR Target Recognition

Remote Sensing, 2021
Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years.
Yanjie Xu, Hao Sun, Jin Chen, Lin Lei, Kefeng Ji, Gangyao Kuang   +5 more
doaj   +1 more source

Advancing Adversarial Robustness Through Adversarial Logit Update

, 2023
Deep Neural Networks are susceptible to adversarial perturbations. Adversarial training and adversarial purification are among the most widely recognized defense strategies. Although these methods have different underlying logic, both rely on absolute logit values to generate label predictions.
Xuan, Hao, Zhu, Peican, Li, Xingyu
openaire   +2 more sources

Adversarially Robust Hyperspectral Image Classification via Random Spectral Sampling and Spectral Shape Encoding

IEEE Access, 2021
Although the hyperspectral image (HSI) classification has adopted deep neural networks (DNNs) and shown remarkable performances, there is a lack of studies of the adversarial vulnerability for the HSI classifications.
Sungjune Park, Hong Joo Lee, Yong Man Ro
doaj   +1 more source

Adversarially Robust Learning via Entropic Regularization [PDF]

Frontiers in Artificial Intelligence, 2022
In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additional entropic regularization. Our loss function considers the contribution of adversarial samples that are drawn from a specially designed distribution in the data space that
Gauri Jagatap, Ameya Joshi, Animesh Basak Chowdhury, Siddharth Garg, Chinmay Hegde   +4 more
openaire   +4 more sources

Robust Algorithms under Adversarial Injections

, 2020
In this paper, we study streaming and online algorithms in the context of randomness in the input. For several problems, a random order of the input sequence---as opposed to the worst-case order---appears to be a necessary evil in order to prove satisfying guarantees. However, algorithmic techniques that work under this assumption tend to be vulnerable
Garg, Paritosh, Kale, Sagar, Rohwedder, Lars, Svensson, Ola   +3 more
openaire   +5 more sources

Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training

Technologies
The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth
Anna Chistyakova, Anastasia Antsiferova, Maksim Khrebtov, Sergey Lavrushkin, Konstantin Arkhipenko, Dmitriy Vatolin, Denis Turdakov   +6 more
doaj   +1 more source

Benchmarking Adversarial Robustness

, 2019
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms.
Dong, Yinpeng, Fu, Qi-An, Yang, Xiao, Pang, Tianyu, Su, Hang, Xiao, Zihao, Zhu, Jun   +6 more
openaire   +2 more sources

Triple Down on Robustness: Understanding the Impact of Adversarial Triplet Compositions on Adversarial Robustness

Machine Learning and Knowledge Extraction
Adversarial training, a widely used technique for fortifying the robustness of machine learning models, has seen its effectiveness further bolstered by modifying loss functions or incorporating additional terms into the training objective.
Sander Joos, Tim Van hamme, Willem Verheyen, Davy Preuveneers, Wouter Joosen   +4 more
doaj   +1 more source

Disentangling Adversarial Robustness and Generalization

, 2019
Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are ...
Hein, Matthias, Schiele, Bernt, Stutz, David   +2 more
core   +1 more source

Adversarially Robust Neural Architectures

IEEE Transactions on Pattern Analysis and Machine Intelligence
13 pages, 5 figures, 8 ...
Minjing Dong, Yanxi Li, Yunhe Wang, Chang Xu   +3 more
openaire   +3 more sources