Noise Augmentation Is All You Need For FGSM Fast Adversarial Training: Catastrophic Overfitting And Robust Overfitting Require Different Augmentation.

[BibT_eX]

[DOI]

Chaoning Zhang

Kang Zhang

CoRR, 2022

How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2022, 2022

Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Investigating Top-k White-Box and Transferable Black-box Attack.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Unrestricted Adversarial Attacks on ImageNet Competition.

[BibT_eX]

[DOI]

CoRR, 2021

A Brief Survey on Deep Learning Based Data Hiding, Steganography and Watermarking.

[BibT_eX]

[DOI]

CoRR, 2021

Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach.

[BibT_eX]

[DOI]

CoRR, 2021

ResNet or DenseNet? Introducing Dense Shortcuts to ResNet.

[BibT_eX]

[DOI]

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Revisiting Batch Normalization for Improving Corruption Robustness.

[BibT_eX]

[DOI]

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Towards Robust Deep Hiding Under Non-Differentiable Distortions for Practical Blind Watermarking.

[BibT_eX]

[DOI]

Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

A Survey on Universal Adversarial Attack.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Motionsnap: A Motion Sensor-Based Approach for Automatic Capture and Editing of Photos and Videos on Smartphones.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Multimedia and Expo, 2021

Universal Adversarial Training with Class-Wise Perturbations.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Multimedia and Expo, 2021

Data-free Universal Adversarial Perturbation and Black-box Attack.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective.

[BibT_eX]

[DOI]

Philipp Benz

Chaoning Zhang

In So Kweon

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Restoration of Video Frames From a Single Blurred Image With Motion Understanding.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs.

[BibT_eX]

[DOI]

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards a Fourier Perspective.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features.

[BibT_eX]

[DOI]

Philipp Benz

Chaoning Zhang

In So Kweon

CoRR, 2020

Data from Model: Extracting Data from Non-robust and Robust Models.

[BibT_eX]

[DOI]

CoRR, 2020

DeepPTZ: Deep Self-Calibration for PTZ Cameras.

[BibT_eX]

[DOI]

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020

Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy.

[BibT_eX]

[DOI]

Proceedings of the NeurIPS 2020 Workshop on Pre-registration in Machine Learning, 2020

UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Double Targeted Universal Adversarial Perturbations.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

CD-UAP: Class Discriminative Universal Adversarial Perturbation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Revisiting Residual Networks with Nonlinear Shortcuts.

[BibT_eX]

[DOI]

Proceedings of the 30th British Machine Vision Conference 2019, 2019

Chaoning Zhang

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