Results 31 to 40 of about 1,209,773 (317)

Adversarial Attacks on Adversarial Bandits

, 2023
Accepted by ICLR ...
Ma, Yuzhe, Zhou, Zhijin
openaire   +2 more sources

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

IEEE Access, 2022
The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in ...
Ibrahim Aliyu, Selinde Van Engelenburg, Muhammed Bashir Mu'Azu, Jinsul Kim, Chang Gyoon Lim   +4 more
doaj   +1 more source

Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification.
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, Xiaolin Hu   +5 more
semanticscholar   +1 more source

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

Conference on Empirical Methods in Natural Language Processing, 2020
While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance.
John X. Morris, Eli Lifland, Jin Yong Yoo, J. Grigsby, Di Jin, Yanjun Qi   +5 more
semanticscholar   +1 more source

Boosting Adversarial Attacks with Momentum

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences.
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li   +6 more
semanticscholar   +1 more source

A Survey of Robustness and Safety of 2D and 3D Deep Learning Models against Adversarial Attacks [PDF]

ACM Computing Surveys, 2023
Benefiting from the rapid development of deep learning, 2D and 3D computer vision applications are deployed in many safe-critical systems, such as autopilot and identity authentication.
Yanjie Li, Bin Xie, Songtao Guo, Yuanyuan Yang, Bin Xiao   +4 more
semanticscholar   +1 more source

Composite Adversarial Attacks

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model ...
Mao, Xiaofeng, Chen, Yuefeng, Wang, Shuhui, Su, Hang, He, Yuan, Xue, Hui   +5 more
openaire   +2 more sources

Defense against Adversarial Patch Attacks for Aerial Image Semantic Segmentation by Robust Feature Extraction

Remote Sensing, 2023
Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety ...
Zhen Wang, Buhong Wang, Chuanlei Zhang, Yaohui Liu   +3 more
doaj   +1 more source

Focused Adversarial Attacks

, 2022
Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations.
Cilloni, Thomas, Walter, Charles, Fleming, Charles   +2 more
openaire   +2 more sources

Benign Adversarial Attack

Proceedings of the 30th ACM International Conference on Multimedia, 2022
ACM MM2022 Brave New ...
Sang, Jitao, Zhao, Xian, Zhang, Jiaming, Lin, Zhiyu   +3 more
openaire   +2 more sources