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Found 6 papers, 5 papers with code
Enhancing the Self-Universality for Transferable Targeted Attacks
Our new attack method is proposed based on the observation that highly universal adversarial perturbations tend to be more transferable for targeted attacks.
Cross-Modal Transferable Adversarial Attacks from Images to Videos
This paper investigates the transferability of adversarial perturbation across different modalities, i. e., leveraging adversarial perturbation generated on white-box image models to attack black-box video models.
Attacking Video Recognition Models with Bullet-Screen Comments
On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90\% fooling rate when attacking three mainstream video recognition models, while only occluding \textless 8\% areas in the video.
Adversarial Attack
Adversarial Attack on Video Classification
+2
Boosting the Transferability of Video Adversarial Examples via Temporal Translation
To this end, we propose to boost the transferability of video adversarial examples for black-box attacks on video recognition models.
Towards Transferable Adversarial Attacks on Vision Transformers
We evaluate the transferability of attacks on state-of-the-art ViTs, CNNs and robustly trained CNNs.
Heuristic Black-box Adversarial Attacks on Video Recognition Models
To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions.
