Results 71 to 80 of about 6,306,959 (200)

Adversarial Machine Learning in Smart Energy Systems [PDF]

, 2019
Smart Energy Systems represent a radical shift in the approach to energy generation and demand, driven by decentralisation of the energy system to large numbers of low-capacity devices.
Bor, Martin, Molineux, Andy, Angelos K. Marnerides, Roedig, Utz, Bor, Martin C., Wattam, Steve, Martin C. Bor, Utz Roedig, Steve Wattam, Marnerides, Angelos K., Marnerides, Angelos, Andy Molineux   +11 more
core   +1 more source

Towards an End-to-End (E2E) Adversarial Learning and Application in the Physical World

Journal of Cybersecurity and Privacy
The traditional process for learning patch-based adversarial attacks, conducted in the digital domain and later applied in the physical domain (e.g., via printed stickers), may suffer reduced performance due to adversarial patches’ limited ...
Dudi Biton, Jacob Shams, Satoru Koda, Asaf Shabtai, Yuval Elovici, Ben Nassi   +5 more
doaj   +1 more source

Ball Bearing Fault Diagnosis Based on Hybrid Adversarial Learning

IEEE Access
Ball bearings are prone to faults in their inner and outer rings and rolling elements. Timely detection of these faults is crucial, especially when adversarial perturbations are present, as deep learning-based fault diagnosis models may misclassify these
Xiaofeng Bai, Qazi Mazhar Ul Haq, Aftab Alam Khan, Chaohui Li, Hind Alsharif, Muhammad Shahid Anwar, Chengwei Li   +6 more
doaj   +1 more source

When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey

Patterns, 2020
With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making, and control for autonomous systems have improved significantly in recent years.
Chongzhen Zhang, Jianrui Wang, Gary G. Yen, Chaoqiang Zhao, Qiyu Sun, Yang Tang, Feng Qian, Jürgen Kurths   +7 more
doaj   +1 more source

Diversity Regularized Adversarial Learning

CoRR, 2019
The two key players in Generative Adversarial Networks (GANs), the discriminator and generator, are usually parameterized as deep neural networks (DNNs). On many generative tasks, GANs achieve state-of-the-art performance but are often unstable to train and sometimes miss modes.
Babajide O. Ayinde, Keishin Nishihama, Jacek M. Zurada   +2 more
openaire   +2 more sources

Research on structure and defense of adversarial example in deep learning

网络与信息安全学报, 2020
With the further promotion of deep learning technology in the fields of computer vision, network security and natural language processing, which has gradually exposed certain security risks.
DUAN Guanghan, SONG Lei, MA Chunguang, WU Peng   +1 more
doaj   +3 more sources

Law and Adversarial Machine Learning

CoRR, 2018
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks ...
Ram Shankar Siva Kumar, David R. O'Brien, Kendra Albert, Salome Vilojen   +3 more
openaire   +3 more sources

Adversarially Learned Anomaly Detection [PDF]

2018 IEEE International Conference on Data Mining (ICDM), 2018
Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this ...
Houssam Zenati, Manon Romain, Chuan Sheng Foo, Bruno Lecouat, Vijay Ramaseshan Chandrasekhar   +4 more
openaire   +3 more sources

On the Generalization Analysis of Adversarial Learning

, 2022
Many recent studies have highlighted the susceptibility of virtually all machine-learning models to adversarial attacks. Adversarial attacks are imperceptible changes to an input example of a given prediction model. Such changes are carefully designed to
Mustafa, Waleed, Kloft, Marius, Lei, Yunwen   +2 more
core  

Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems

, 2020
Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data ...
Vähäkainu, Petri, Lehto, Martti, Kariluoto, Antti   +2 more
core   +1 more source