Adversarial Robustness in the Real World


Can Optical Trojans Assist Adversarial Perturbations?
Adith Boloor,
Tong Wu,
Patrick Naughton,
Ayan Chakrabarti,
Xuan Zhang,
Yevgeniy Vorobeychik
[pdf] [supp]
[bibtex]
@InProceedings{Boloor_2021_ICCV, author = {Boloor, Adith and Wu, Tong and Naughton, Patrick and Chakrabarti, Ayan and Zhang, Xuan and Vorobeychik, Yevgeniy}, title = {Can Optical Trojans Assist Adversarial Perturbations?}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {122-131} }

On Adversarial Robustness: A Neural Architecture Search Perspective
Chaitanya Devaguptapu,
Devansh Agarwal,
Gaurav Mittal,
Pulkit Gopalani,
Vineeth N Balasubramanian
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Devaguptapu_2021_ICCV, author = {Devaguptapu, Chaitanya and Agarwal, Devansh and Mittal, Gaurav and Gopalani, Pulkit and Balasubramanian, Vineeth N}, title = {On Adversarial Robustness: A Neural Architecture Search Perspective}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {152-161} }

Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation
Yuan Wu,
Diana Inkpen,
Ahmed El-Roby
[pdf]
[bibtex]
@InProceedings{Wu_2021_ICCV, author = {Wu, Yuan and Inkpen, Diana and El-Roby, Ahmed}, title = {Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {132-141} }

Evasion Attack STeganography: Turning Vulnerability of Machine Learning To Adversarial Attacks Into a Real-World Application
Salah Ghamizi,
Maxime Cordy,
Mike Papadakis,
Yves Le Traon
[pdf]
[bibtex]
@InProceedings{Ghamizi_2021_ICCV, author = {Ghamizi, Salah and Cordy, Maxime and Papadakis, Mike and Le Traon, Yves}, title = {Evasion Attack STeganography: Turning Vulnerability of Machine Learning To Adversarial Attacks Into a Real-World Application}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {31-40} }

Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Single-Source Domain Generalization
Thomas Duboudin,
Emmanuel Dellandrea,
Corentin Abgrall,
Gilles Henaff,
Liming Chen
[pdf]
[bibtex]
@InProceedings{Duboudin_2021_ICCV, author = {Duboudin, Thomas and Dellandrea, Emmanuel and Abgrall, Corentin and Henaff, Gilles and Chen, Liming}, title = {Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Single-Source Domain Generalization}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {51-60} }

Trojan Signatures in DNN Weights
Greg Fields,
Mohammad Samragh,
Mojan Javaheripi,
Farinaz Koushanfar,
Tara Javidi
[pdf] [arXiv]
[bibtex]
@InProceedings{Fields_2021_ICCV, author = {Fields, Greg and Samragh, Mohammad and Javaheripi, Mojan and Koushanfar, Farinaz and Javidi, Tara}, title = {Trojan Signatures in DNN Weights}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {12-20} }

On the Effect of Pruning on Adversarial Robustness
Artur Jordao,
Helio Pedrini
[pdf] [arXiv]
[bibtex]
@InProceedings{Jordao_2021_ICCV, author = {Jordao, Artur and Pedrini, Helio}, title = {On the Effect of Pruning on Adversarial Robustness}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1-11} }

Optical Adversarial Attack
Abhiram Gnanasambandam,
Alex M. Sherman,
Stanley H. Chan
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Gnanasambandam_2021_ICCV, author = {Gnanasambandam, Abhiram and Sherman, Alex M. and Chan, Stanley H.}, title = {Optical Adversarial Attack}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {92-101} }

Enhancing Adversarial Robustness via Test-Time Transformation Ensembling
Juan C. Perez,
Motasem Alfarra,
Guillaume Jeanneret,
Laura Rueda,
Ali Thabet,
Bernard Ghanem,
Pablo Arbelaez
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Perez_2021_ICCV, author = {Perez, Juan C. and Alfarra, Motasem and Jeanneret, Guillaume and Rueda, Laura and Thabet, Ali and Ghanem, Bernard and Arbelaez, Pablo}, title = {Enhancing Adversarial Robustness via Test-Time Transformation Ensembling}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {81-91} }

AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
Yuzhen Ding,
Nupur Thakur,
Baoxin Li
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Ding_2021_ICCV, author = {Ding, Yuzhen and Thakur, Nupur and Li, Baoxin}, title = {AdvFoolGen: Creating Persistent Troubles for Deep Classifiers}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {142-151} }

Countering Adversarial Examples: Combining Input Transformation and Noisy Training
Cheng Zhang,
Pan Gao
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Zhang_2021_ICCV, author = {Zhang, Cheng and Gao, Pan}, title = {Countering Adversarial Examples: Combining Input Transformation and Noisy Training}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {102-111} }

A Hierarchical Assessment of Adversarial Severity
Guillaume Jeanneret,
Juan C. Perez,
Pablo Arbelaez
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Jeanneret_2021_ICCV, author = {Jeanneret, Guillaume and Perez, Juan C. and Arbelaez, Pablo}, title = {A Hierarchical Assessment of Adversarial Severity}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {61-70} }

Patch Attack Invariance: How Sensitive Are Patch Attacks to 3D Pose?
Max Lennon,
Nathan Drenkow,
Phil Burlina
[pdf] [arXiv]
[bibtex]
@InProceedings{Lennon_2021_ICCV, author = {Lennon, Max and Drenkow, Nathan and Burlina, Phil}, title = {Patch Attack Invariance: How Sensitive Are Patch Attacks to 3D Pose?}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {112-121} }

Detecting and Segmenting Adversarial Graphics Patterns From Images
Xiangyu Qu,
Stanley H. Chan
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Qu_2021_ICCV, author = {Qu, Xiangyu and Chan, Stanley H.}, title = {Detecting and Segmenting Adversarial Graphics Patterns From Images}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {71-80} }

Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
Nathan Inkawhich,
Kevin J Liang,
Jingyang Zhang,
Huanrui Yang,
Hai Li,
Yiran Chen
[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Inkawhich_2021_ICCV, author = {Inkawhich, Nathan and Liang, Kevin J and Zhang, Jingyang and Yang, Huanrui and Li, Hai and Chen, Yiran}, title = {Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {41-50} }

Impact of Colour on Robustness of Deep Neural Networks
Kanjar De,
Marius Pedersen
[pdf]
[bibtex]
@InProceedings{De_2021_ICCV, author = {De, Kanjar and Pedersen, Marius}, title = {Impact of Colour on Robustness of Deep Neural Networks}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {21-30} }