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Adil Karjauv
Adil Karjauv
Machine Learning R&D, Qualcomm
kaist.ac.kr의 이메일 확인됨
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Udh: Universal deep hiding for steganography, watermarking, and light field messaging
C Zhang, P Benz, A Karjauv, G Sun, IS Kweon
Advances in Neural Information Processing Systems 33, 10223-10234, 2020
1482020
A survey on universal adversarial attack
C Zhang, P Benz, C Lin, A Karjauv, J Wu, IS Kweon
arXiv preprint arXiv:2103.01498, 2021
952021
Revisiting batch normalization for improving corruption robustness
P Benz, C Zhang, A Karjauv, IS Kweon
Proceedings of the IEEE/CVF winter conference on applications of computer …, 2021
842021
Adversarial robustness comparison of vision transformer and mlp-mixer to cnns
P Benz, S Ham, C Zhang, A Karjauv, IS Kweon
arXiv preprint arXiv:2110.02797, 2021
812021
Robustness may be at odds with fairness: An empirical study on class-wise accuracy
P Benz, C Zhang, A Karjauv, IS Kweon
NeurIPS 2020 Workshop on pre-registration in machine learning, 325-342, 2021
512021
Universal adversarial perturbations through the lens of deep steganography: Towards a fourier perspective
C Zhang, P Benz, A Karjauv, IS Kweon
Proceedings of the AAAI conference on artificial intelligence 35 (4), 3296-3304, 2021
502021
Data-free universal adversarial perturbation and black-box attack
C Zhang, P Benz, A Karjauv, IS Kweon
Proceedings of the IEEE/CVF international conference on computer vision …, 2021
492021
Towards robust deep hiding under non-differentiable distortions for practical blind watermarking
C Zhang, A Karjauv, P Benz, IS Kweon
Proceedings of the 29th ACM international conference on multimedia, 5158-5166, 2021
472021
Investigating top-k white-box and transferable black-box attack
C Zhang, P Benz, A Karjauv, JW Cho, K Zhang, IS Kweon
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
372022
Universal adversarial training with class-wise perturbations
P Benz, C Zhang, A Karjauv, IS Kweon
2021 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2021
312021
Towards robust data hiding against (jpeg) compression: A pseudo-differentiable deep learning approach
C Zhang, A Karjauv, P Benz, IS Kweon
arXiv preprint arXiv:2101.00973, 2020
192020
Robustness comparison of vision transformer and mlp-mixer to cnns
P Benz, C Zhang, S Ham, A Karjauv, IS Kweon
Proceedings of the CVPR 2021 Workshop on Adversarial Machine Learning in …, 2021
142021
Backpropagating smoothly improves transferability of adversarial examples
C Zhang, P Benz, G Cho, A Karjauv, S Ham, CH Youn, IS Kweon
CVPR 2021 Workshop Workshop on Adversarial Machine Learning in Real-World …, 2021
92021
Adversarial robustness comparison of vision transformer and mlp-mixer to cnns. arXiv 2021
P Benz, S Ham, C Zhang, A Karjauv, IS Kweon
arXiv preprint arXiv:2110.02797, 0
8
Trade-off between accuracy, robustness, and fairness of deep classifiers
P Benz, C Zhang, S Ham, A Karjauv, G Cho, IS Kweon
Workshop on Adversarial Machine Learning in Real-World Computer Vision …, 2021
32021
Object-centric diffusion for efficient video editing
K Kahatapitiya, A Karjauv, D Abati, F Porikli, YM Asano, A Habibian
arXiv preprint arXiv:2401.05735, 2024
22024
CHEN gui-lin
Q ZHANG, G WANG
Study on improving image registration accuracy of fy-4 multi-channel …, 2005
22005
Simple Techniques are Sufficient for Boosting Adversarial Transferability
C Zhang, P Benz, A Karjauv, IS Kweon, CS Hong
Proceedings of the 31st ACM International Conference on Multimedia, 8486-8494, 2023
2023
Motionsnap: A Motion Sensor-Based Approach for Automatic Capture and Editing of Photos and Videos on Smartphones
A Karjauv, S Bakhtiyarov, C Zhang, JC Bazin, IS Kweon
2021 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2021
2021
Towards robust deep hiding under non-differentiable distortions for practical blind watermarking
A Karjauv
한국과학기술원, 2021
2021
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