Search Results for author:
Found 4 papers, 1 papers with code
Improving Adversarial Transferability of Visual-Language Pre-training Models through Collaborative Multimodal Interaction
Existing work rarely studies the transferability of attacks on VLP models, resulting in a substantial performance gap from white-box attacks.
Efficient Decision-based Black-box Patch Attacks on Video Recognition
First, STDE introduces target videos as patch textures and only adds patches on keyframes that are adaptively selected by temporal difference.
Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization
To tackle these issues, we propose Global Momentum Initialization (GI) to suppress gradient elimination and help search for the global optimum.
Efficient universal shuffle attack for visual object tracking
Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
