Results 121 to 130 of about 4,317 (163)

Evaluating gait system vulnerabilities through PPO and GAN-generated adversarial attacks. [PDF]

Sci Rep
Saoudi EM, Jaafari J, Jai Andaloussi S.
europepmc   +1 more source

PromptGuard a structured framework for injection resilient language models. [PDF]

Sci Rep
Alzahrani A.
europepmc   +1 more source

When collaboration fails: persuasion driven adversarial influence in multi agent large language model debate. [PDF]

Sci Rep
Kraidia I, Qaddara I, Almutairi A, Alzaben N, Belhouari SB.   +4 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Adversarial Attacks and Defenses on Graphs

ACM SIGKDD Explorations Newsletter, 2021
Deep 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, Yaxin Li 0001, Han Xu 0002, Yiqi Wang 0001, Shuiwang Ji, Charu Aggarwal 0001, Jiliang Tang   +6 more
openaire   +1 more source

Sinkhorn Adversarial Attack and Defense

IEEE Transactions on Image Processing, 2022
Adversarial 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, Bing-Hua Lai, Hui-Fuang Ng, Wei-Yang Lin, Ming-Ching Chang   +4 more
openaire   +1 more source

Adversarial Attacks and Defenses

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection.
Han Xu 0002, Yaxin Li 0001, Wei Jin 0009, Jiliang Tang   +3 more
openaire   +1 more source

DeepRobust: a Platform for Adversarial Attacks and Defenses

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization.
Yaxin Li 0001, Wei Jin 0009, Han Xu 0002, Jiliang Tang   +3 more
openaire   +2 more sources

MTD-AD: Moving Target Defense as Adversarial Defense

IEEE Transactions on Dependable and Secure Computing
Network Intrusion Detection Systems (NIDSes) are increasingly incorporating Machine Learning (ML) and Deep Learning (DL) algorithms for detecting network intrusions. However, ML/DL algorithms are susceptible to adversarial examples, which can lead to the misclassification of input data.
Ke He, Dan Dongseong Kim, Muhammad Rizwan Asghar   +2 more
openaire   +5 more sources