Results 101 to 110 of about 6,306,959 (200)

POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

IEEE Access
Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee, Jin-Seoung Kim, Han-Jin Lee, Seok-Hwan Choi   +3 more
doaj   +1 more source

Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling [PDF]

, 2017
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convo-lutional networks and ...
Wu, Jiajun, Freeman, William T., Zhang, Chengkai, Xue, Tianfan, Tenenbaum, Joshua B.   +4 more
core  

Adversarial Attacks in Computer Vision: A Survey [PDF]

Jisuanji gongcheng
Deep learning has driven the development of artificial intelligence, which is widely used in computer vision. It provides breakthroughs and remarkable results in complex tasks such as image recognition, object detection, object tracking, and face ...
QIN Yingxin, ZHANG Kejia, PAN Haiwei, JU Yahao
doaj   +1 more source

Financial Development in Adversarial and Inquisitorial Legal Systems [PDF]

This paper analyzes how the adversarial and inquisitorial evidence collection procedures affect financial development. In investigating the true returns of insolvent entrepreneurs, the adversarial procedure relies on lawyers whereas the inquisitorial ...
Massenot Baptiste
core  

Adversarial Online Learning with noise

CoRR, 2018
We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise rate and a variable noise rate. Our
Alon Resler, Yishay Mansour
openaire   +4 more sources

Fake news detection with GAN-augmented contrastive learning and multimodal attention

Cybersecurity
The rapid proliferation of fake news in digital media has emerged as a major threat to information credibility and public trust. Although recent advances have explored multimodal learning for fake news detection, existing models often fail to effectively
Cong Wu, Jing Chen, Yebo Feng, Ju Jia, Tingting Xu, Zijian Zhang, Jiahua Xu, Teng Li, Yang Liu   +8 more
doaj   +1 more source

Enhancing quantum adversarial robustness by randomized encodings

Physical Review Research
The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving certain challenging problems ...
Weiyuan Gong, Dong Yuan, Weikang Li, Dong-Ling Deng   +3 more
doaj   +1 more source

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

Future Internet
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran, Annalisa Appice, Donato Malerba   +2 more
doaj   +1 more source

Automated federated learning‐based adversarial attack and defence in industrial control systems

IET Cyber-systems and Robotics
With the development of deep learning and federated learning (FL), federated intrusion detection systems (IDSs) based on deep learning have played a significant role in securing industrial control systems (ICSs).
Guo‐Qiang Zeng, Jun‐Min Shao, Kang‐Di Lu, Guang‐Gang Geng, Jian Weng   +4 more
doaj   +1 more source

Regularization for Adversarial Robust Learning

CoRR
51 pages, 5 ...
Jie Wang, Rui Gao, Yao Xie
openaire   +3 more sources