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Ensemble Adversarial Example Defense Based on Generative Adversarial Network
工程科学与技术, 2022 Given the bottlenecks of existing adversarial example defense schemes, such as insufficient defense capability and high time consumption, an ensemble adversarial example defense scheme based on the generative adversarial network was proposed in this ...
Tianjie CAO +5 more
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Defending Against Adversarial Fingerprint Attacks Based on Deep Image Prior
IEEE Access, 2023 Recently, deep learning-based biometric authentication systems, especially fingerprint authentication, have been used widely in real-world. However, these systems are vulnerable to adversarial attacks which prevent deep learning models from ...
Hwajung Yoo +4 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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Defense Against Universal Adversarial Perturbations [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recent advances in Deep Learning show the existence of image-agnostic quasi-imperceptible perturbations that when applied to `any' image can fool a state-of-the-art network classifier to change its prediction about the image label. These `Universal Adversarial Perturbations' pose a serious threat to the success of Deep Learning in practice.
Naveed Akhtar, Jian Liu 0014, Ajmal Mian
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, 2023 This paper presents a robust adversarial defense mechanism, Noisy-Defense Variational Auto-Encoder (ND-VAE), that combines the strengths of Nouveau VAE (NVAE) and Defense-VAE to effectively eliminate adversarial attacks from contaminated images.
Jalalinour, Shayan, Rekabdar, Banafsheh
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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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Certified Defenses for Adversarial Patches
CoRR, 2020 International Conference on Learning Representations, ICLR ...
Chiang, Ping-yeh +5 more
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Scaling provable adversarial defenses
CoRR, 2018 Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in an effort to scale these approaches to substantially larger models, we extend previous work in three ...
Eric Wong 0001 +3 more
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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 Attacks and Defenses in Deep Learning
Engineering, 2020 With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms.
Kui Ren +3 more
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