Sukrut Rao (PhD Student)
Sukrut Sridhar Rao
- Address
- Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken - Location
- E1 4 - 628
- Phone
- +49 681 9325 2146
- Fax
- +49 681 9325 2099
- Get email via email
Personal Information
Publications
Rao, S., Böhle, M., & Schiele, B. (2022). Towards Better Understanding Attribution Methods. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA: IEEE. doi:10.1109/CVPR52688.2022.00998
Export
BibTeX
@inproceedings{Rao_CVPR2022,
TITLE = {Towards Better Understanding Attribution Methods},
AUTHOR = {Rao, Sukrut and B{\"o}hle, Moritz and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-6654-6946-3},
DOI = {10.1109/CVPR52688.2022.00998},
PUBLISHER = {IEEE},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)},
PAGES = {10213--10222},
ADDRESS = {New Orleans, LA, USA},
}
Endnote
%0 Conference Proceedings
%A Rao, Sukrut
%A Böhle, Moritz
%A Schiele, Bernt
%+ Computer Graphics, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Towards Better Understanding Attribution Methods :
%G eng
%U http://hdl.handle.net/21.11116/0000-000A-6F91-6
%R 10.1109/CVPR52688.2022.00998
%D 2022
%B 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%Z date of event: 2022-06-19 - 2022-06-24
%C New Orleans, LA, USA
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 10213 - 10222
%I IEEE
%@ 978-1-6654-6946-3
Rao, S., Stutz, D., & Schiele, B. (2020). Adversarial Training against Location-Optimized Adversarial Patches. Retrieved from https://arxiv.org/abs/2005.02313
(arXiv: 2005.02313) Abstract
Deep neural networks have been shown to be susceptible to adversarial<br>examples -- small, imperceptible changes constructed to cause<br>mis-classification in otherwise highly accurate image classifiers. As a<br>practical alternative, recent work proposed so-called adversarial patches:<br>clearly visible, but adversarially crafted rectangular patches in images. These<br>patches can easily be printed and applied in the physical world. While defenses<br>against imperceptible adversarial examples have been studied extensively,<br>robustness against adversarial patches is poorly understood. In this work, we<br>first devise a practical approach to obtain adversarial patches while actively<br>optimizing their location within the image. Then, we apply adversarial training<br>on these location-optimized adversarial patches and demonstrate significantly<br>improved robustness on CIFAR10 and GTSRB. Additionally, in contrast to<br>adversarial training on imperceptible adversarial examples, our adversarial<br>patch training does not reduce accuracy.<br>
Export
BibTeX
@online{Rao_arXiv2005.02313,
TITLE = {Adversarial Training against Location-Optimized Adversarial Patches},
AUTHOR = {Rao, Sukrut and Stutz, David and Schiele, Bernt},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2005.02313},
EPRINT = {2005.02313},
EPRINTTYPE = {arXiv},
YEAR = {2020},
ABSTRACT = {Deep neural networks have been shown to be susceptible to adversarial<br>examples -- small, imperceptible changes constructed to cause<br>mis-classification in otherwise highly accurate image classifiers. As a<br>practical alternative, recent work proposed so-called adversarial patches:<br>clearly visible, but adversarially crafted rectangular patches in images. These<br>patches can easily be printed and applied in the physical world. While defenses<br>against imperceptible adversarial examples have been studied extensively,<br>robustness against adversarial patches is poorly understood. In this work, we<br>first devise a practical approach to obtain adversarial patches while actively<br>optimizing their location within the image. Then, we apply adversarial training<br>on these location-optimized adversarial patches and demonstrate significantly<br>improved robustness on CIFAR10 and GTSRB. Additionally, in contrast to<br>adversarial training on imperceptible adversarial examples, our adversarial<br>patch training does not reduce accuracy.<br>},
}
Endnote
%0 Report
%A Rao, Sukrut
%A Stutz, David
%A Schiele, Bernt
%+ Computer Graphics, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Adversarial Training against Location-Optimized Adversarial Patches :
%G eng
%U http://hdl.handle.net/21.11116/0000-0007-80D0-C
%U https://arxiv.org/abs/2005.02313
%D 2020
%X Deep neural networks have been shown to be susceptible to adversarial<br>examples -- small, imperceptible changes constructed to cause<br>mis-classification in otherwise highly accurate image classifiers. As a<br>practical alternative, recent work proposed so-called adversarial patches:<br>clearly visible, but adversarially crafted rectangular patches in images. These<br>patches can easily be printed and applied in the physical world. While defenses<br>against imperceptible adversarial examples have been studied extensively,<br>robustness against adversarial patches is poorly understood. In this work, we<br>first devise a practical approach to obtain adversarial patches while actively<br>optimizing their location within the image. Then, we apply adversarial training<br>on these location-optimized adversarial patches and demonstrate significantly<br>improved robustness on CIFAR10 and GTSRB. Additionally, in contrast to<br>adversarial training on imperceptible adversarial examples, our adversarial<br>patch training does not reduce accuracy.<br>
%K Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Cryptography and Security, cs.CR,Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
Rao, S., Stutz, D., & Schiele, B. (2021). Adversarial Training Against Location-Optimized Adversarial Patches. In Computer Vision -- ECCV Workshops 2020. Glasgow, UK: Springer. doi:10.1007/978-3-030-68238-5_32
Export
BibTeX
@inproceedings{DBLP:conf/eccv/RaoSS20,
TITLE = {Adversarial Training Against Location-Optimized Adversarial Patches},
AUTHOR = {Rao, Sukrut and Stutz, David and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-3-030-68237-8},
DOI = {10.1007/978-3-030-68238-5_32},
PUBLISHER = {Springer},
YEAR = {2020},
MARGINALMARK = {$\bullet$},
DATE = {2021},
BOOKTITLE = {Computer Vision -- ECCV Workshops 2020},
EDITOR = {Bartoli, Adrian and Fusiello, Andrea},
PAGES = {429--448},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {12539},
ADDRESS = {Glasgow, UK},
}
Endnote
%0 Conference Proceedings
%A Rao, Sukrut
%A Stutz, David
%A Schiele, Bernt
%+ Computer Graphics, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Adversarial Training Against Location-Optimized Adversarial Patches :
%G eng
%U http://hdl.handle.net/21.11116/0000-0008-1662-1
%R 10.1007/978-3-030-68238-5_32
%D 2021
%B 16th European Conference on Computer Vision
%Z date of event: 2020-08-23 - 2020-08-28
%C Glasgow, UK
%B Computer Vision -- ECCV Workshops 2020
%E Bartoli, Adrian; Fusiello, Andrea
%P 429 - 448
%I Springer
%@ 978-3-030-68237-8
%B Lecture Notes in Computer Science
%N 12539