Adil Karjauv

Orcid: 0009-0002-4639-3076

According to our database1, Adil Karjauv authored at least 14 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Object-Centric Diffusion for Efficient Video Editing.
CoRR, 2024

2023
Simple Techniques are Sufficient for Boosting Adversarial Transferability.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

2022
Investigating Top-k White-Box and Transferable Black-box Attack.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach.
CoRR, 2021

Revisiting Batch Normalization for Improving Corruption Robustness.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Towards Robust Deep Hiding Under Non-Differentiable Distortions for Practical Blind Watermarking.
Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

A Survey on Universal Adversarial Attack.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Motionsnap: A Motion Sensor-Based Approach for Automatic Capture and Editing of Photos and Videos on Smartphones.
Proceedings of the 2021 IEEE International Conference on Multimedia and Expo, 2021

Universal Adversarial Training with Class-Wise Perturbations.
Proceedings of the 2021 IEEE International Conference on Multimedia and Expo, 2021

Data-free Universal Adversarial Perturbation and Black-box Attack.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs.
Proceedings of the 32nd British Machine Vision Conference 2021, 2021

Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards a Fourier Perspective.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy.
Proceedings of the NeurIPS 2020 Workshop on Pre-registration in Machine Learning, 2020

UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020


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