CVPR 2022 Open Access Repository

The Art of Robustness: Devil and Angel in Adversarial Machine Learning

Strengthening the Transferability of Adversarial Examples Using Advanced Looking Ahead and Self-CutMix: Donggon Jang,

Sanghyeok Son,

Dae-Shik Kim; [pdf] [supp]
[bibtex]
@InProceedings{Jang_2022_CVPR, author = {Jang, Donggon and Son, Sanghyeok and Kim, Dae-Shik}, title = {Strengthening the Transferability of Adversarial Examples Using Advanced Looking Ahead and Self-CutMix}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {148-155} }
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems: Furkan Mumcu,

Keval Doshi,

Yasin Yilmaz; [pdf] [arXiv]
[bibtex]
@InProceedings{Mumcu_2022_CVPR, author = {Mumcu, Furkan and Doshi, Keval and Yilmaz, Yasin}, title = {Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {206-213} }
The Risk and Opportunity of Adversarial Example in Military Field: Yuwei Chen; [pdf]
[bibtex]
@InProceedings{Chen_2022_CVPR, author = {Chen, Yuwei}, title = {The Risk and Opportunity of Adversarial Example in Military Field}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {100-107} }
PAT: Pseudo-Adversarial Training for Detecting Adversarial Videos: Nupur Thakur,

Baoxin Li; [pdf] [arXiv]
[bibtex]
@InProceedings{Thakur_2022_CVPR, author = {Thakur, Nupur and Li, Baoxin}, title = {PAT: Pseudo-Adversarial Training for Detecting Adversarial Videos}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {131-138} }
Adversarial Robustness Through the Lens of Convolutional Filters: Paul Gavrikov,

Janis Keuper; [pdf] [arXiv]
[bibtex]
@InProceedings{Gavrikov_2022_CVPR, author = {Gavrikov, Paul and Keuper, Janis}, title = {Adversarial Robustness Through the Lens of Convolutional Filters}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {139-147} }
Towards Comprehensive Testing on the Robustness of Cooperative Multi-Agent Reinforcement Learning: Jun Guo,

Yonghong Chen,

Yihang Hao,

Zixin Yin,

Yin Yu,

Simin Li; [pdf] [arXiv]
[bibtex]
@InProceedings{Guo_2022_CVPR, author = {Guo, Jun and Chen, Yonghong and Hao, Yihang and Yin, Zixin and Yu, Yin and Li, Simin}, title = {Towards Comprehensive Testing on the Robustness of Cooperative Multi-Agent Reinforcement Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {115-122} }
Exploring Robustness Connection Between Artificial and Natural Adversarial Examples: Akshay Agarwal,

Nalini Ratha,

Mayank Vatsa,

Richa Singh; [pdf]
[bibtex]
@InProceedings{Agarwal_2022_CVPR, author = {Agarwal, Akshay and Ratha, Nalini and Vatsa, Mayank and Singh, Richa}, title = {Exploring Robustness Connection Between Artificial and Natural Adversarial Examples}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {179-186} }
AugLy: Data Augmentations for Adversarial Robustness: Zoë Papakipos,

Joanna Bitton; [pdf]
[bibtex]
@InProceedings{Papakipos_2022_CVPR, author = {Papakipos, Zo\"e and Bitton, Joanna}, title = {AugLy: Data Augmentations for Adversarial Robustness}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {156-163} }
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection: Umar Khalid,

Ashkan Esmaeili,

Nazmul Karim,

Nazanin Rahnavard; [pdf] [arXiv]
[bibtex]
@InProceedings{Khalid_2022_CVPR, author = {Khalid, Umar and Esmaeili, Ashkan and Karim, Nazmul and Rahnavard, Nazanin}, title = {RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {164-171} }
Robustness and Adaptation to Hidden Factors of Variation: William Paul,

Philippe Burlina; [pdf] [arXiv]
[bibtex]
@InProceedings{Paul_2022_CVPR, author = {Paul, William and Burlina, Philippe}, title = {Robustness and Adaptation to Hidden Factors of Variation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {123-130} }
Poisons That Are Learned Faster Are More Effective: Pedro Sandoval-Segura,

Vasu Singla,

Liam Fowl,

Jonas Geiping,

Micah Goldblum,

David Jacobs,

Tom Goldstein; [pdf]
[bibtex]
@InProceedings{Sandoval-Segura_2022_CVPR, author = {Sandoval-Segura, Pedro and Singla, Vasu and Fowl, Liam and Geiping, Jonas and Goldblum, Micah and Jacobs, David and Goldstein, Tom}, title = {Poisons That Are Learned Faster Are More Effective}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {198-205} }
Privacy Leakage of Adversarial Training Models in Federated Learning Systems: Jingyang Zhang,

Yiran Chen,

Hai Li; [pdf] [arXiv]
[bibtex]
@InProceedings{Zhang_2022_CVPR, author = {Zhang, Jingyang and Chen, Yiran and Li, Hai}, title = {Privacy Leakage of Adversarial Training Models in Federated Learning Systems}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {108-114} }
An Empirical Study of Data-Free Quantization's Tuning Robustness: Hong Chen,

Yuxuan Wen,

Yifu Ding,

Zhen Yang,

Yufei Guo,

Haotong Qin; [pdf]
[bibtex]
@InProceedings{Chen_2022_CVPR, author = {Chen, Hong and Wen, Yuxuan and Ding, Yifu and Yang, Zhen and Guo, Yufei and Qin, Haotong}, title = {An Empirical Study of Data-Free Quantization's Tuning Robustness}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {172-178} }
CorrGAN: Input Transformation Technique Against Natural Corruptions: Mirazul Haque,

Christof J. Budnik,

Wei Yang; [pdf] [arXiv]
[bibtex]
@InProceedings{Haque_2022_CVPR, author = {Haque, Mirazul and Budnik, Christof J. and Yang, Wei}, title = {CorrGAN: Input Transformation Technique Against Natural Corruptions}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {194-197} }
Generalizing Adversarial Explanations With Grad-CAM: Tanmay Chakraborty,

Utkarsh Trehan,

Khawla Mallat,

Jean-Luc Dugelay; [pdf] [arXiv]
[bibtex]
@InProceedings{Chakraborty_2022_CVPR, author = {Chakraborty, Tanmay and Trehan, Utkarsh and Mallat, Khawla and Dugelay, Jean-Luc}, title = {Generalizing Adversarial Explanations With Grad-CAM}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {187-193} }