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Adil Karjauv
Adil Karjauv
Machine Learning R&D, Qualcomm
kaist.ac.kr의 이메일 확인됨
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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
462021
A survey on universal adversarial attack
C Zhang, P Benz, C Lin, A Karjauv, J Wu, IS Kweon
arXiv preprint arXiv:2103.01498, 2021
452021
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
442020
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
262021
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
252021
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
162021
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
162021
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
132021
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
112021
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
82020
Robustness comparison of vision transformer and MLP-Mixer to CNNs
P Benz, C Zhang, S Ham, A Karjauv, IS Kweon
CVPR 2021 Workshop on Adversarial Machine Learning in Real-World Computer …, 2021
72021
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
42022
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
32021
Trade-off between accuracy, robustness, and fairness of deep classifiers
P Benz, C Zhang, S Ham, A Karjauv, G Cho, IS Kweon
22021
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
Is FGSM Optimal or Necessary for L∞ Adversarial Attack?
C Zhang, A Karjauv, P Benz, S Ham, G Cho, CH Youn, IS Kweon
Workshop on Adversarial Machine Learning in Real-World Computer Vision …, 2021
2021
Towards robust deep hiding under non-differentiable distortions for practical blind watermarking
A Karjauv
한국과학기술원, 2021
2021
Data-free Universal Adversarial Perturbation and Black-box Attack Supplementary Material
C Zhang, P Benz, A Karjauv, IS Kweon
MapIt: Collaborative student summary with concept maps
A Yerembessov, A Karjauv, A Poppele, N Kim
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학술자료 1–19