Results 21 to 30 of about 16,674 (259)

Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning

2022 IEEE International Conference on Image Processing (ICIP), 2022
Appeared in ICIP ...
Jie Zhang 0081, Lei Zhang 0197, Gang Li, Chao Wu 0001   +3 more
openaire   +2 more sources

A Robust Adversarial Example Attack Based on Video Augmentation

Applied Sciences, 2023
Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin, Yixiao Xu, Teng Hu, Xiaolei Liu   +3 more
doaj   +1 more source

A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation

IEEE Access, 2020
Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN ...
Jianyi Liu, Yu Tian, Ru Zhang, Youqiang Sun, Chan Wang   +4 more
doaj   +1 more source

Generating Adversarial Examples with Adversarial Networks [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Chaowei Xiao, Bo Li 0026, Jun-Yan Zhu, Warren He, Mingyan Liu, Dawn Song   +5 more
openaire   +2 more sources

Recovery of Adversarial Examples Based on SmsGAN [PDF]

Zhengzhou Daxue xuebao. Gongxue ban, 2021
Due to adversarial examples′ serious interference to the detection models based on deep learning, a recovery method of adversarial examples based on stochastic multihlter statistical generative adversarial network (SmsGAN) was proposed in this work.
ZHAO Junjie, WANG Jinwei
doaj   +1 more source

Adversarial Examples and Metrics

CoRR, 2020
25 pages, 1 figure, under submission, fixe typos from previous ...
Nico Döttling, Kathrin Grosse, Michael Backes 0001, Ian M. Molloy   +3 more
openaire   +2 more sources

“Adversarial Examples” for Proof-of-Learning

2022 IEEE Symposium on Security and Privacy (SP), 2022
To appear in the 43rd IEEE Symposium on Security and ...
Rui Zhang 0118, Jian Liu 0012, Yuan Ding, Zhibo Wang 0001, Qingbiao Wu, Kui Ren 0001   +5 more
openaire   +2 more sources

Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks

Big Data and Cognitive Computing, 2020
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using
Shayan Taheri, Aminollah Khormali, Milad Salem, Jiann-Shiun Yuan   +3 more
doaj   +1 more source

Hadamard’s Defense Against Adversarial Examples

IEEE Access, 2021
Adversarial images have become an increasing concern in real-world image recognition applications with deep neural networks (DNN). We observed that all the architectures in DNN use one-hot encoding after a softmax layer.
Angello Hoyos, Ubaldo Ruiz, Edgar Chavez
doaj   +1 more source

Unrestricted Adversarial Examples

CoRR, 2018
We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries.
Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul F. Christiano, Ian J. Goodfellow   +5 more
openaire   +2 more sources