Results 21 to 30 of about 82,924 (315)
Open-Set Adversarial Defense [PDF]
, 2020 Accepted by ECCV ...
Rui Shao 0001 +3 more
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Attacking Adversarial Attacks as A Defense
CoRR, 2021 It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, failure cases of defense still broadly exist. In this work, we find that the adversarial attacks can also be vulnerable to small perturbations.
Boxi Wu +8 more
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Adversarial Ranking Attack and Defense [PDF]
, 2020 Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Mo Zhou +4 more
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Survey on adversarial attacks and defense of face forgery and detection
网络与信息安全学报, 2023 Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and ...
Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO
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Robust Rumor Detection based on Multi-Defense Model Ensemble
Applied Artificial Intelligence, 2023 The development of adversarial technology, represented by adversarial text, has brought new challenges to rumor detection based on deep learning. In order to improve the robustness of rumor detection models under adversarial conditions, we propose a ...
Fan Yang, Shaomei Li
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Adversarial Attacks Defense Method Based on Multiple Filtering and Image Rotation
Discrete Dynamics in Nature and Society, 2022 Adversarial examples in an image classification task cause neural networks to predict incorrect class labels with high confidence. Many applications related to image classification, such as self-driving and facial recognition, have been seriously ...
Feng Li, Xuehui Du, Liu Zhang
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CoRR, 2023 The vulnerability of Deep Neural Networks (DNNs) to adversarial examples has been confirmed. Existing adversarial defenses primarily aim at preventing adversarial examples from attacking DNNs successfully, rather than preventing their generation. If the generation of adversarial examples is unregulated, images within reach are no longer secure and pose
Jinwei Wang +5 more
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Textual Adversarial Training Method Based on Distributed Perturbation [PDF]
Jisuanji gongcheng, 2023 Text adversarial defense aims to enhance the resilience of neural network models against different adversarial attacks. The current text confrontation defense methods are usually only effective against certain specific confrontation attacks and have ...
Zhidong SHEN, Hengxian YUE
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The Defense of Adversarial Example with Conditional Generative Adversarial Networks [PDF]
Security and Communication Networks, 2020 Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause of the adversarial examples is unclear.
Fangchao Yu +3 more
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Adversarial attack and defense on graph neural networks: a survey
网络与信息安全学报, 2021 For the numerous existing adversarial attack and defense methods on GNN, the main adversarial attack and defense algorithms of GNN were reviewed comprehensively, as well as robustness analysis techniques.Besides, the commonly used benchmark datasets and ...
Jinyin CHEN +4 more
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