Search Results for author:
Found 7 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.
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
To evaluate the effectiveness of our general framework OneTracker, which is consisted of Foundation Tracker and Prompt Tracker, we conduct extensive experiments on 6 popular tracking tasks across 11 benchmarks and our OneTracker outperforms other models and achieves state-of-the-art performance.
Delving into Decision-based Black-box Attacks on Semantic Segmentation
We conduct adversarial robustness evaluation on 5 models from Cityscapes and ADE20K under 8 attacks.
Exploring Decision-based Black-box Attacks on Face Forgery Detection
Further, the fake faces by our method can pass face forgery detection and face recognition, which exposes the security problems of face forgery detectors.
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.
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation
Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT).
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.
