Publications
⠀* indicates equal contribution.
2026
2025
- CVPR 2025 WorkshopAdapToR: Adaptive Token Reduction for Video Diffusion TransformersIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025
2024
2023
- ACM MM 2023Simple Techniques are Sufficient for Boosting Adversarial TransferabilityIn Proceedings of the 31st ACM International Conference on Multimedia (ACM MM), 2023
2022
- CVPR 2022Investigating Top-k White-Box and Transferable Black-box AttackIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
2021
- BMVC 2021Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsIn British Machine Vision Conference (BMVC), 2021
- ACM MM 2021Towards Robust Deep Hiding Under Non-Differentiable Distortions for Practical Blind WatermarkingIn Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), 2021
- ICCV 2021Data-free Universal Adversarial Perturbation and Black-box AttackIn Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021
- IJCAI 2021A Survey on Universal Adversarial AttackIn International Joint Conference on Artificial Intelligence (IJCAI), 2021
- ICME 2021Universal Adversarial Training with Class-Wise PerturbationsIn 2021 IEEE International Conference on Multimedia and Expo (ICME), 2021
- ICME 2021Motionsnap: A Motion Sensor-Based Approach for Automatic Capture and Editing of Photos and Videos on SmartphonesIn 2021 IEEE International Conference on Multimedia and Expo (ICME), 2021
- AAAI 2021Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier PerspectiveIn Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021
- WACV 2021Revisiting Batch Normalization for Improving Corruption RobustnessIn Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021
- CVPR Workshop 2021Is FGSM Optimal or Necessary for L∞ Adversarial Attack?In Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges (CVPR AML-CV Workshop), 2021
- ICLR Workshop 2021On Strength and Transferability of Adversarial Examples: Stronger Attack Transfers BetterIn Robust and Reliable Machine Learning in the Real World Workshop (ICLR Workshop), 2021
- ICLR Workshop 2021Stochastic Depth Boosts Transferability of Non-targeted and Targeted Adversarial AttacksIn Robust and Reliable Machine Learning in the Real World Workshop (ICLR Workshop), 2021
- ICLR Workshop 2021Towards Data-free Universal Adversarial Perturbations with Artificial Jigsaw ImagesIn Robust and Reliable Machine Learning in the Real World Workshop (ICLR Workshop), 2021