Results 1 to 10 of about 85,460 (123)
Adversarial attacks against supervised machine learning based network intrusion detection systems. [PDF]
PLoS ONE, 2022 Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the ...
Ebtihaj Alshahrani +3 more
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Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
IEEE Communications Letters, 2019 We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks.
Meysam Sadeghi, Erik G Larsson
exaly +3 more sources
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
IEEE Access, 2018 Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security.
Naveed Akhtar, Ajmal Mian
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Black Box Adversarial Attack Starting Point Promotion Method Based on Mobility Between Models [PDF]
Jisuanji gongcheng, 2021 In order to efficiently find the adversarial samples under the decision-based black box attacks, a method using the mobility between models is proposed to enhance the adversarial starting point. The mobility is used to circularly superimpose interference
CHEN Xiaonan, HU Jianmin, ZHANG Benjun, CHEN Ailing
doaj +1 more source
A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
Applied Sciences, 2021 Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make
Zhirui Luo, Qingqing Li, Jun Zheng
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Remote Sensing, 2023 Aerial Image Semantic segmentation based on convolution neural networks (CNNs) has made significant process in recent years. Nevertheless, their vulnerability to adversarial example attacks could not be neglected.
Zhen Wang +3 more
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Adversarial attacks and defenses in deep learning
网络与信息安全学报, 2020 The adversarial example is a modified image that is added imperceptible perturbations, which can make deep neural networks decide wrongly. The adversarial examples seriously threaten the availability of the system and bring great security risks to the ...
LIU Ximeng +2 more
doaj +3 more sources
TextFirewall: Omni-Defending Against Adversarial Texts in Sentiment Classification
IEEE Access, 2021 Sentiment classification has been broadly applied in real life, such as product recommendation and opinion-oriented analysis. Unfortunately, the widely employed sentiment classification systems based on deep neural networks (DNNs) are susceptible to ...
Wenqi Wang +3 more
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GANBA: Generative Adversarial Network for Biometric Anti-Spoofing
Applied Sciences, 2022 Automatic speaker verification (ASV) is a voice biometric technology whose security might be compromised by spoofing attacks. To increase the robustness against spoofing attacks, presentation attack detection (PAD) or anti-spoofing systems for detecting ...
Alejandro Gomez-Alanis +2 more
doaj +1 more source
Multi-Class Triplet Loss With Gaussian Noise for Adversarial Robustness
IEEE Access, 2020 Deep Neural Networks (DNNs) classifiers performance degrades under adversarial attacks, such attacks are indistinguishably perturbed relative to the original data.
Benjamin Appiah +4 more
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