Results 241 to 250 of about 5,384,791 (287)

A large language model-based detection method for poisoning attacks in recommender systems. [PDF]

Sci Rep
Liang F, Hao Y, Yuan G, Li J, Feng L.
europepmc   +1 more source

Unsupervised Annotation Transfer in Phase-Contrast Microscopy Using a CycleGAN. [PDF]

Sensors (Basel)
Al-Hamadani MNA, Hadjidemetriou S, Szeman-Nagy G, Skourides PA, Hajdu A, Harangi B.   +5 more
europepmc   +1 more source

Leveraging generative adversarial networks and SE-UNet for high-precision tobacco leaf image segmentation and blend uniformity detection. [PDF]

Sci Rep
Ding M, Peng X, Ji X, Chen S, Li Q, Zou H, Fan Z, Wang S.   +7 more
europepmc   +1 more source

Cyborg-swarm cooperation and game via affective-based brain-machine interface. [PDF]

Natl Sci Rev
Chen Z, Zhang L, Chen G, Liu H, Wang Z, Zhao X, Guo S, Zhao T, Sun M, Liang W, Qin L, Zhang M, Liu L, Wang W.   +13 more
europepmc   +1 more source

Evaluation of Explainable Artificial Intelligence in IoT Intrusion Detection Systems Under DeepFool Adversarial Conditions. [PDF]

Sensors (Basel)
Munilla J, Khammas RM.
europepmc   +1 more source

Beyond 2D and CNNs: Future Directions for Deep Learning in Periodontal Diagnostics. [PDF]

Int Dent J
Meng K, Lv S, Liu J.
europepmc   +1 more source

AED-PADA: Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation [PDF]

ACM Transactions on Multimedia Computing, Communications and Applications
Adversarial example detection, which can be conveniently applied in many scenarios, is important in the area of adversarial defense. Unfortunately, existing detection methods suffer from poor generalization performance, because their training process ...
Yunhong Wang, Yuanfang Guo, Ruijie Yang
exaly   +2 more sources

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Adversarial Examples for Malware Detection

2017
Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick McDaniel   +4 more
openaire   +1 more source

Adversarial examples for network intrusion detection systems

Journal of Computer Security, 2022
Machine learning-based network intrusion detection systems have demonstrated state-of-the-art accuracy in flagging malicious traffic. However, machine learning has been shown to be vulnerable to adversarial examples, particularly in domains such as image recognition.
Ryan Sheatsley, Nicolas Papernot, Michael J. Weisman, Gunjan Verma, Patrick D. McDaniel   +4 more
openaire   +1 more source