Search Results for author: Bangjie Yin

Found 9 papers, 4 papers with code

Rethinking Impersonation and Dodging Attacks on Face Recognition Systems

no code implementations17 Jan 2024 Fengfan Zhou, Qianyu Zhou, Bangjie Yin, Hui Zheng, Xuequan Lu, Lizhuang Ma, Hefei Ling

Then, Biased Gradient Adaptation is presented to adapt the adversarial examples to traverse the decision boundaries of both the attacker and victim by adding perturbations favoring dodging attacks on the vacated regions, preserving the prioritized features of the original perturbations while boosting dodging performance.

Face Recognition

Contrastive Pseudo Learning for Open-World DeepFake Attribution

1 code implementation ICCV 2023 Zhimin Sun, Shen Chen, Taiping Yao, Bangjie Yin, Ran Yi, Shouhong Ding, Lizhuang Ma

The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques.

DeepFake Detection Face Swapping +1

Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition

1 code implementation CVPR 2023 Zexin Li, Bangjie Yin, Taiping Yao, Juefeng Guo, Shouhong Ding, Simin Chen, Cong Liu

A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i. e., inaccessible gradient and parameter information to attackers.

Adversarial Attack Attribute +1

Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition

no code implementations13 Oct 2022 Shuai Jia, Bangjie Yin, Taiping Yao, Shouhong Ding, Chunhua Shen, Xiaokang Yang, Chao Ma

For face recognition attacks, existing methods typically generate the l_p-norm perturbations on pixels, however, resulting in low attack transferability and high vulnerability to denoising defense models.

Adversarial Attack Attribute +2

Structure Destruction and Content Combination for Face Anti-Spoofing

no code implementations22 Jul 2021 Ke-Yue Zhang, Taiping Yao, Jian Zhang, Shice Liu, Bangjie Yin, Shouhong Ding, Jilin Li

In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types with the assistance of auxiliary supervision.

Face Anti-Spoofing Face Verification +1

Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition

1 code implementation7 May 2021 Bangjie Yin, Wenxuan Wang, Taiping Yao, Junfeng Guo, Zelun Kong, Shouhong Ding, Jilin Li, Cong Liu

Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples.

Adversarial Attack Face Generation +2

Delving into Data: Effectively Substitute Training for Black-box Attack

no code implementations CVPR 2021 Wenxuan Wang, Bangjie Yin, Taiping Yao, Li Zhang, Yanwei Fu, Shouhong Ding, Jilin Li, Feiyue Huang, xiangyang xue

Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models.

Adversarial Attack

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