Search Results for author: Jiadong Lin

Found 6 papers, 5 papers with code

Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability

1 code implementation CVPR 2022 Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, Kun He

The black-box adversarial attack has attracted impressive attention for its practical use in the field of deep learning security.

Adversarial Attack

Stochastic Variance Reduced Ensemble Adversarial Attack

no code implementations29 Sep 2021 Jiadong Lin, Yifeng Xiong, Min Zhang, John E. Hopcroft, Kun He

Black-box adversarial attack has attracted much attention for its practical use in deep learning applications, and it is very challenging as there is no access to the architecture and weights of the target model.

Adversarial Attack

Boosting Adversarial Transferability through Enhanced Momentum

1 code implementation19 Mar 2021 Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, Kun He

Various momentum iterative gradient-based methods are shown to be effective to improve the adversarial transferability.

Adversarial Attack

Robust Local Features for Improving the Generalization of Adversarial Training

1 code implementation ICLR 2020 Chuanbiao Song, Kun He, Jiadong Lin, Li-Wei Wang, John E. Hopcroft

We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples.

Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

3 code implementations ICLR 2020 Jiadong Lin, Chuanbiao Song, Kun He, Li-Wei Wang, John E. Hopcroft

While SIM is based on our discovery on the scale-invariant property of deep learning models, for which we leverage to optimize the adversarial perturbations over the scale copies of the input images so as to avoid "overfitting" on the white-box model being attacked and generate more transferable adversarial examples.

Adversarial Attack

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