Results 41 to 50 of about 834,802 (152)

Sparsity based defense against adversarial examples: v1.0

, 2020
Sparsity-based defense against adversarial attacks on machine learning classifiers. Contains code for the following papers: S. Gopalakrishnan, Z. Marzi, U. Madhow, R.
Soorya Gopalakrishnan
core   +1 more source

Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks

Big Data and Cognitive Computing, 2020
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using
Shayan Taheri, Aminollah Khormali, Milad Salem, Jiann-Shiun Yuan   +3 more
doaj   +1 more source

Text Adversarial Purification as Defense against Adversarial Attacks

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
Accepted by ACL2023 main ...
Linyang Li, Demin Song, Xipeng Qiu
openaire   +2 more sources

Internal Wasserstein Distance for Adversarial Attack and Defense [PDF]

, 2023
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks that would trigger misclassification of DNNs but may be imperceptible to human perception.
Wang, Qicheng, Tan, Mingkui, Zhang, Shuhai, Cao, Jiezhang, Li, Jincheng, Xiang, Yang   +5 more
core  

Defending Against Adversarial Attacks with Camera Image Pipelines

, 2023
Existing neural networks for computer vision tasks are vulnerable to adversarial attacks: adding imperceptible perturbations to the input images can fool these models into making a false prediction on an image that was correctly predicted without the ...
Zhang, Yuxuan
core  

Adversarial Robustness Enhancement of UAV-Oriented Automatic Image Recognition Based on Deep Ensemble Models

Remote Sensing, 2023
Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances.
Zihao Lu, Hao Sun, Yanjie Xu
doaj   +1 more source

Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

CoRR, 2020
Advances in the development of adversarial attacks have been fundamental to the progress of adversarial defense research. Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Adversarial attacks are often generated by maximizing standard losses such as the cross-entropy loss or maximum ...
Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R.   +3 more
openaire   +3 more sources

Adversarial Recommender Systems: Attack, Defense, and Advances

, 2022
Adversarial machine learning is the research field investigating vulnerabilities inherent to machine learning systems’ design and ways to defend against them.
Di Noia, Tommaso, Deldjoo, Yashar, Merra, Felice Antonio, Anelli, Vito Walter   +3 more
core   +1 more source

LPF-Defense: 3D adversarial defense based on frequency analysis

PLOS ONE, 2023
The 3D point clouds are increasingly being used in various application including safety-critical fields. It has recently been demonstrated that deep neural networks can successfully process 3D point clouds. However, these deep networks can be misclassified via 3D adversarial attacks intentionality designed to perturb some point cloud’s features.
Hanieh Naderi, Kimia Noorbakhsh, Arian Etemadi, Shohreh Kasaei   +3 more
openaire   +4 more sources

Multi-Line Defense Against Windows Adversarial Malware by Using Windows PE Information

IEEE Access
Deep learning has recently been in the spotlight among malware detection researchers in the sense that its training-based robust decision process can lead to efficient and effective malware detection.
Hannah Ho, Jun-Won Ho, Sungjin Ho
doaj   +1 more source