Search Results for author: Hengchang Guo

Found 5 papers, 3 papers with code

Practical Deep Dispersed Watermarking with Synchronization and Fusion

1 code implementation23 Oct 2023 Hengchang Guo, Qilong Zhang, Junwei Luo, Feng Guo, Wenbin Zhang, Xiaodong Su, Minglei Li

Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5. 28\% and 5. 93\% against single and combined attacks, respectively, and show less file size increment and better visual quality.

Towards Transferable Targeted Adversarial Examples

1 code implementation CVPR 2023 Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, Kui Ren

In this paper, we propose the Transferable Targeted Adversarial Attack (TTAA), which can capture the distribution information of the target class from both label-wise and feature-wise perspectives, to generate highly transferable targeted adversarial examples.

Adversarial Attack

Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks

no code implementations ICCV 2023 Xue Wang, Zhibo Wang, Haiqin Weng, Hengchang Guo, Zhifei Zhang, Lu Jin, Tao Wei, Kui Ren

Considering the insufficient study on such complex causal questions, we make the first attempt to explain different causal questions by contrastive explanations in a unified framework, ie., Counterfactual Contrastive Explanation (CCE), which visually and intuitively explains the aforementioned questions via a novel positive-negative saliency-based explanation scheme.


Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training

no code implementations5 Jun 2022 Guodong Cao, Zhibo Wang, Xiaowei Dong, Zhifei Zhang, Hengchang Guo, Zhan Qin, Kui Ren

However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those non-robust but highly predictive features.

Knowledge Distillation

Feature Importance-aware Transferable Adversarial Attacks

3 code implementations ICCV 2021 Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, Kui Ren

More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image.

Feature Importance

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