Sizhe Chen | DeepAI

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∙ 02/23/2023

Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective

Although fast adversarial training provides an efficient approach for bu...

1 Zhengbao He, et al. ∙

∙ 11/22/2022

Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors

As data become increasingly vital for deep learning, a company would be ...

0 Sizhe Chen, et al. ∙

∙ 08/12/2022

Unifying Gradients to Improve Real-world Robustness for Deep Networks

The wide application of deep neural networks (DNNs) demands an increasin...

0 Yingwen Wu, et al. ∙

∙ 05/24/2022

One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks

Unlearnable examples (ULEs) aim to protect data from unauthorized usage ...

0 Shutong Wu, et al. ∙

∙ 05/24/2022

Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks

The score-based query attacks (SQAs) pose practical threats to deep neur...

0 Sizhe Chen, et al. ∙

∙ 11/24/2021

Subspace Adversarial Training

Single-step adversarial training (AT) has received wide attention as it ...

0 Tao Li, et al. ∙

∙ 05/31/2021

Dominant Patterns: Critical Features Hidden in Deep Neural Networks

In this paper, we find the existence of critical features hidden in Deep...

0 Zhixing Ye, et al. ∙

∙ 05/31/2021

QueryNet: An Efficient Attack Framework with Surrogates Carrying Multiple Identities

Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversaria...

0 Sizhe Chen, et al. ∙

∙ 02/20/2021

Going Far Boosts Attack Transferability, but Do Not Do It

Deep Neural Networks (DNNs) could be easily fooled by Adversarial Exampl...

0 Sizhe Chen, et al. ∙

∙ 08/16/2020

Attack on Multi-Node Attention for Object Detection

This paper focuses on high-transferable adversarial attacks on detection...

74 Sizhe Chen, et al. ∙

∙ 03/04/2020

Double Backpropagation for Training Autoencoders against Adversarial Attack

Deep learning, as widely known, is vulnerable to adversarial samples. Th...

0 Chengjin Sun, et al. ∙

∙ 03/04/2020

Type I Attack for Generative Models

Generative models are popular tools with a wide range of applications. N...

0 Chengjin Sun, et al. ∙

∙ 01/21/2020

Generate High-Resolution Adversarial Samples by Identifying Effective Features

As the prevalence of deep learning in computer vision, adversarial sampl...

19 Sizhe Chen, et al. ∙

∙ 01/16/2020

Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet

Adversarial attacks on deep neural networks (DNNs) have been found for s...

2 Sizhe Chen, et al. ∙

∙ 12/16/2019

DAmageNet: A Universal Adversarial Dataset

It is now well known that deep neural networks (DNNs) are vulnerable to ...

0 Sizhe Chen, et al. ∙

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