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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

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

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   +3 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   +3 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   +2 more sources

Boundary Black-box Adversarial Example Generation Algorithm on Video Recognition Models [PDF]

Jisuanji kexue
With the rapid development of deep learning,neural networks are widely used in various fields.However,neural networks still face the problem of adversarial attacks.Among all types of adversarial attacks,the boundary black-box attack can only obtain the ...
JING Yulin, WU Lijun, LI Zhiyuan, DENG Qi
doaj   +2 more sources

AdvGuard: Fortifying Deep Neural Networks Against Optimized Adversarial Example Attack

IEEE Access
Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, video recognition, and pattern analysis. However, they are vulnerable to adversarial example attacks.
Hyun Kwon, Jun Lee
doaj   +3 more sources

Generating adversarial examples without specifying a target model [PDF]

PeerJ Computer Science, 2021
Adversarial examples are regarded as a security threat to deep learning models, and there are many ways to generate them. However, most existing methods require the query authority of the target during their work.
Gaoming Yang, Mingwei Li, Xianjing Fang, Ji Zhang, Xingzhu Liang   +4 more
doaj   +2 more sources

Clustering Approach for Detecting Multiple Types of Adversarial Examples

Sensors, 2022
With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi, Tae-u Bahk, Sungyong Ahn, Yoon-Ho Choi   +3 more
doaj   +1 more source

A Universal Detection Method for Adversarial Examples and Fake Images

Sensors, 2022
Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning technologies. For examples, state-of-the-art deep-learning technologies are vulnerable
Jiewei Lai, Yantong Huo, Ruitao Hou, Xianmin Wang   +3 more
doaj   +1 more source