Results 21 to 30 of about 177,286 (215)

Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples

IEEE Access, 2023
Adversarial examples (AEs) are typical model evasion attacks and security threats in deep neural networks (DNNs). One of the countermeasures is adversarial training (AT), and it trains DNNs by using a training dataset containing AEs to achieve robustness
Yuta Fukuda, Kota Yoshida, Takeshi Fujino   +2 more
doaj   +1 more source

Adversarial Example Games

CoRR, 2020
Appears in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton   +6 more
openaire   +3 more sources

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 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

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

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

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

Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network

Remote Sensing, 2021
Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems.
Chuan Du, Lei Zhang
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 Generation Method Based on Random Translation Transformation [PDF]

Jisuanji gongcheng, 2022
The image classification model based on Deep Neural Network(DNN) can recognize images with a recognition degree that is even higher than that of human eyes.However, it is vulnerable to attacks from adversarial examples because of the fragility of the ...
LI Zheming, ZHANG Hengwei, MA Junqiang, WANG Jindong, YANG Bo
doaj   +1 more source