Results 81 to 90 of about 6,306,959 (200)

A Speech Adversarial Sample Detection Method Based on Manifold Learning

Mathematics
Deep learning-based models have achieved impressive results across various practical fields. However, these models are susceptible to attacks. Recent research has demonstrated that adversarial samples can significantly decrease the accuracy of deep ...
Xiao Ma, Dongliang Xu, Chenglin Yang, Panpan Li, Dong Li   +4 more
doaj   +1 more source

SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL

Tạp chí Khoa học Đại học Đà Lạt
Artificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models.
Thanh Son Phan, Quang Hua Ta, Duy Trung Pham, Phi Ho Truong   +3 more
doaj   +1 more source

The Applications of Generative Adversarial Network in Surgical Videos

, 2023
Unstructured data e.g. images and videos are widely used in the medical field. Because the generative adversarial network (GAN) has the ability to process images with fewer labels and better feature extraction, the application of GAN in surgical video ...
Wang, Peiyan
core   +1 more source

Bilevel Models for Adversarial Learning and a Case Study

Mathematics
Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial ...
Yutong Zheng, Qingna Li
doaj   +1 more source

Multi-Domain Adversarial Learning

CoRR, 2019
Accepted at ICLR ...
Schoenauer Sebag, Alice, Heinrich, Louise, Schoenauer, Marc, Sebag, Michèle, Wu, Lani, Altschuler, Steven   +5 more
openaire   +5 more sources

Defending Poisoning Attacks in Federated Learning via Adversarial Training Method

, 2020
Recently, federated learning has shown its significant advantages in protecting training data privacy by maintaining a joint model across multiple clients.
Chen, Bing, Di Wu, Liu, Chengyong, Chengyong Liu, Zhang, Jiale, Jiale Zhang, Bing Chen, Wu, Di   +7 more
core   +1 more source

FAMF: Robust Feature-Level Adversarial Attack on Metric-Based Few-Shot Learning Models

IEEE Access
Few-shot learning has emerged as the primary approach for tasks with extremely limited training data. In particular, metric-based few-shot learning has been highlighted for enabling effective generalization to new tasks by comparing feature similarity ...
Gwang-Nam Kim, Han-Ju Lee, In-Woong Jeong, Jin-Myeong Shin, Seok-Hwan Choi   +4 more
doaj   +1 more source

Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion. [PDF]

In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models.
Istrate, Gabriel, Marathe, Madhav V., Ravi, S.S.   +2 more
core  

RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning

Future Internet, 2019
With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a
Yong Fang, Cheng Huang, Yijia Xu, Yang Li   +3 more
doaj   +1 more source

Exploiting Machine Learning to Subvert Your Spam Filter [PDF]

, 2008
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—
Nelson, Blaine, Tygar, J. D., Xia, Kai, Rubinstein, Benjamin I. P., Sutton, Charles, Barreno, Marco, Chi, Fuching Jack, Joseph, Anthony D., Saini, Udam   +8 more
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