Results 11 to 20 of about 5,389,393 (319)

On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification

Applied Sciences, 2020
State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks.
Sanglee Park, Jungmin So
doaj   +5 more sources

Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network

IEEE Access, 2018
Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon, Yongchul Kim, Ki-Woong PARK, Hyunsoo Yoon, Daeseon Choi   +4 more
doaj   +5 more sources

A survey of practical adversarial example attacks

Cybersecurity, 2018
Adversarial examples revealed the weakness of machine learning techniques in terms of robustness, which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning.
Lu Sun, Mingtian Tan, Zhe Zhou
doaj   +3 more sources

Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences [PDF]

Sensors
The threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection.
Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu Liang   +4 more
doaj   +4 more sources

A Novel Adversarial Example Detection Method Based on Frequency Domain Reconstruction for Image Sensors [PDF]

Sensors
Convolutional neural networks (CNNs) have been extensively used in numerous remote sensing image detection tasks owing to their exceptional performance.
Shuaina Huang, Zhiyong Zhang, Bin Song
doaj   +3 more sources

Random Untargeted Adversarial Example on Deep Neural Network

Symmetry, 2018
Deep neural networks (DNNs) have demonstrated remarkable performance in machine learning areas such as image recognition, speech recognition, intrusion detection, and pattern analysis.
Hyun Kwon, Yongchul Kim, Hyunsoo Yoon
exaly   +3 more sources

Code Difference Guided Adversarial Example Generation for Deep Code Models [PDF]

International Conference on Automated Software Engineering, 2023
Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics constraints, the adversarial example generation techniques in other domains ...
Zhao Tian, Junjie Chen, Zhi Jin
semanticscholar   +3 more sources

Adversarial example defense based on image reconstruction [PDF]

PeerJ Computer Science, 2021
The rapid development of deep neural networks (DNN) has promoted the widespread application of image recognition, natural language processing, and autonomous driving.
Yu(AUST) Zhang, Huan Xu, Chengfei Pei, Gaoming Yang   +3 more
doaj   +3 more sources

Targeted Speech Adversarial Example Generation With Generative Adversarial Network

IEEE Access, 2020
Although neural network-based speech recognition models have enjoyed significant success in many acoustic systems, they are susceptible to be attacked by the adversarial examples.
Donghua Wang, Li Dong, Rangding Wang, Diqun Yan, Jie Wang   +4 more
doaj   +2 more sources

Learning Universal Adversarial Perturbation by Adversarial Example

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level ...
Maosen Li, Yanhua Yang, Kun-Juan Wei, Xu Yang, Heng Huang   +4 more
semanticscholar   +2 more sources