8 citations · 26 across the 14 of their papers we have counts for
6 papers · 1 filter
How Robust is Google's Bard to Adversarial Image Attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen +6
Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However,…
Enhancing Adversarial Attacks: The Similar Target Method
Shuo Zhang, Ziruo Wang, Zikai Zhou +1
Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applications and raising security concerns. An intriguing property of adversarial exampl…
AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework
Zikai Zhou, Shuo Zhang, Ziruo Wang +1
The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvant…
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang +4
Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training.…
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
Shitong Shao, Xu Dai, Lujun Li +3
Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves perf…
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
Huanran Chen, Yichi Zhang, Yinpeng Dong +3
It is widely recognized that deep learning models lack robustness to adversarial examples. An intriguing property of adversarial examples is that they can transfer across different…