Results 271 to 280 of about 1,209,773 (317)

Black-box adversarial attack defense approach: An empirical analysis from cybersecurity perceptive

Kousik Barik, Sanjay Misra, Inés López-Baldominos   +2 more
openalex   +1 more source

Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks

Anastasia Antsiferova, Khaled Abud, Aleksandr Gushchin, Ekaterina Shumitskaya, Sergey Lavrushkin, Dmitriy Vatolin   +5 more
openalex   +2 more sources

Comprehensive Analysis on Laser Spots Adversarial Attacks Using Genetic Algorithm

Youssef Mansour, Ayad Turky, Mohamed Hashem, Imad Afyouni, Ali Bou Nassif, Ismail Shahin, Ashraf Elnagar   +6 more
openalex   +1 more source

Crafting imperceptible on-manifold adversarial attacks for tabular data

Zhipeng He, Alexander Stevens, Chun Ouyang, Johannes De Smedt, Alistair Barros, Catarina Moreira   +5 more
openalex   +2 more sources

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack

arXiv.org
In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes.
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu, Chenyu Zhang, Jiahao Huang, Jianlong Zhou, Fang Chen   +6 more
semanticscholar   +3 more sources

SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS

Darpan International Research Analysis
In recent years, the fields of Artificial Intelligence (AI) and Deep learning (DL) techniques along with Neural Networks (NNs) have shown great progress and scope for future research.
Akshat Jain, Sanskar Agarwal, Armaan Pareek, Vanshika Singh   +3 more
semanticscholar   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Natural Adversarial Attacks

2024 International Conference on Machine Learning and Applications (ICMLA)
We study robust, efficient, and stealthy attacks on object detector types of classifiers. Such adversarial attacks are meant to suppress the correct classification of objects in a real world scenario, in our case the classification of road signs by a ...
Michael J. Hughes, Sven Schewe
semanticscholar   +2 more sources

Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks

arXiv.org, 2023
Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows.
Erfan Shayegani, Md. Abdullah Al Mamun, Yu Fu, Pedram Zaree, Yue Dong, Nael B. Abu-Ghazaleh   +5 more
semanticscholar   +1 more source

The Impact of Adversarial Attacks on Federated Learning: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Federated learning (FL) has emerged as a powerful machine learning technique that enables the development of models from decentralized data sources. However, the decentralized nature of FL makes it vulnerable to adversarial attacks.
Kummari Naveen Kumar, C. K. Mohan, Linga Reddy Cenkeramaddi   +2 more
semanticscholar   +1 more source