Results 11 to 20 of about 82,924 (315)
Research Progress of Adversarial Defenses on Graphs
Jisuanji kexue yu tansuo, 2021 Graph neural networks (GNN) have been successfully applied in complex tasks in many fields, but recent studies show that GNN is vulnerable to graph adversarial attacks, leading to severe performance degradation.
LI Penghui, ZHAI Zhengli, FENG Shu
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Muzammal Naseer +4 more
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Adversarial example defense algorithm for MNIST based on image reconstruction
网络与信息安全学报, 2022 With the popularization of deep learning, more and more attention has been paid to its security issues.The adversarial sample is to add a small disturbance to the original image, which can cause the deep learning model to misclassify the image, which ...
Zhongyuan QIN +3 more
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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 +3 more
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CoRR, 2023 In response to the rapidly evolving nature of adversarial attacks against visual classifiers, numerous defenses have been proposed to generalize against as many known attacks as possible. However, designing a defense method that generalizes to all types of attacks is unrealistic, as the environment in which the defense system operates is dynamic.
Qian Wang 0001 +8 more
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IEEE Access, 2022 An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety.
Seok-Hwan Choi +3 more
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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 +3 more
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Adversarial Attacks and Defenses
ACM SIGKDD Explorations Newsletter, 2021 Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Ninghao Liu +4 more
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Demotivate Adversarial Defense in Remote Sensing [PDF]
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021 Convolutional neural networks are currently the state-of-the-art algorithms for many remote sensing applications such as semantic segmentation or object detection. However, these algorithms are extremely sensitive to over-fitting, domain change and adversarial examples specifically designed to fool them.
Adrien Chan-Hon-Tong +2 more
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A Defense Method Against FGSM Adversarial Attack [PDF]
Jisuanji gongcheng, 2021 Intelligent ship recognition has been widely used in the military,but it also brings increasingly serious security issues.Even the high performance classification models are still vulnerable to the attacks from adversarial examples.For Fast Gradient Sign
WANG Xiaopeng, LUO Wei, QIN Ke, YANG Jintao, WANG Min
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