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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

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

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

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

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

Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview

Applied Sciences, 2021
Voice Processing Systems (VPSes), now widely deployed, have become deeply involved in people’s daily lives, helping drive the car, unlock the smartphone, make online purchases, etc.
Xiaojiao Chen, Sheng Li, Hao Huang
doaj   +1 more source