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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   +2 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

Detecting Adversarial Examples through Nonlinear Dimensionality Reduction [PDF]

, 2019
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques.
Bacciu, Davide, Biggio, Battista, Crecchi, Francesco   +2 more
core   +5 more sources

Securing IoT Vision Systems: An Unsupervised Framework for Adversarial Example Detection Integrating Spatial Prototypes and Multidimensional Statistics [PDF]

Sensors
The deployment of deep learning models in Internet of Things (IoT) systems is increasingly threatened by adversarial attacks. To address the challenge of effectively detecting adversarial examples generated by Generative Adversarial Networks (AdvGANs ...
Naile Wang, Jian Li, Chunhui Zhang, Dejun Zhang   +3 more
doaj   +2 more sources

Adversarial Examples Detection Beyond Image Space [PDF]

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
To appear in ICASSP ...
Chen, Kejiang, Chen, Yuefeng, Zhou, Hang, Qin, Chuan, Mao, Xiaofeng, Zhang, Weiming, Yu, Nenghai   +6 more
openaire   +2 more sources

Adversarial Examples Detection Method Based on Image Denoising and Compression [PDF]

Jisuanji gongcheng, 2023
Numerous deep learning achievements in the field of computer vision have been widely applied in real life. However, adversarial examples can lead to false positives in deep learning models with high confidence, resulting in serious security consequences.
Feiyu WANG, Fan ZHANG, Jiayu DU, Hongle LEI, Xiaofeng QI
doaj   +1 more source

Imperceptible Adversarial Examples For Fake Image Detection [PDF]

2021 IEEE International Conference on Image Processing (ICIP), 2021
Accepted by ICIP ...
Liao, Quanyu, Li, Yuezun, Wang, Xin, Kong, Bin, Zhu, Bin, Lyu, Siwei, Yin, Youbing, Song, Qi, Wu, Xi   +8 more
openaire   +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

Adversarial Example Remaining Availability and Functionality [PDF]

Jisuanji kexue yu tansuo, 2022
Malware detection method based on gray images has received a lot of attention because it does not require disassembly and can obtain a high detection accuracy.
XIAO Mao, GUO Chun, SHEN Guowei, JIANG Chaohui
doaj   +1 more source