6 citations · 11 across the 15 of their papers we have counts for
16 papers
Bridging Data Trials and Task Barriers: A Unified Framework for Sketch Biometric Identification
Decheng Liu, Bin Hu, Xinbo Gao +4
Different from existing cross-modality identification tasks (e.g., heterogeneous face recognition, sketch re-identification, etc.), we introduce a novel yet practical setting for t…
A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation
Dawei Zhou, Suzhi Gang, Decheng Liu +3
Malicious applications of visual manipulation have raised serious threats to the security and reputation of users in many fields. To alleviate these issues, adversarial noise-based…
Towards Generalized Proactive Defense against Face Swapping with Contour-Hybrid Watermark
Ruiyang Xia, Dawei Zhou, Decheng Liu +4
Face swapping, recognized as a privacy and security concern, has prompted considerable defensive research. With the advancements in AI-generated content, the discrepancies between…
Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance
Jiahua Xu, Dawei Zhou, Lei Hu +3
Multimodal medical images play a crucial role in the precise and comprehensive clinical diagnosis. Diffusion model is a powerful strategy to synthesize the required medical images.…
Improving Adversarial Robustness via Phase and Amplitude-aware Prompting
Yibo Xu, Dawei Zhou, Decheng Liu +1
Deep neural networks are found to be vulnerable to adversarial perturbations. The prompt-based defense has been increasingly studied due to its high efficiency. However, existing p…
Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
Jiahua Xu, Dawei Zhou, Lei Hu +5
Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been e…
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
Jiacheng Zhang, Feng Liu, Dawei Zhou +2
Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations ar…
AFD: Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement
Nuoyan Zhou, Dawei Zhou, Decheng Liu +2
Adversarial fine-tuning methods enhance adversarial robustness via fine-tuning the pre-trained model in an adversarial training manner. However, we identify that some specific late…
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria
Nuoyan Zhou, Nannan Wang, Decheng Liu +2
Deep neural networks are vulnerable to adversarial noise. Adversarial Training (AT) has been demonstrated to be the most effective defense strategy to protect neural networks from…
Gradient constrained sharpness-aware prompt learning for vision-language models
Liangchen Liu, Nannan Wang, Dawei Zhou +4
This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i.e., improving the performance on unseen classes while maintaining…
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization
Yulong Yang, Chenhao Lin, Qian Li +6
Quantized neural networks (QNNs) have received increasing attention in resource-constrained scenarios due to their exceptional generalizability. However, their robustness against r…
Personalized Federated Learning under Mixture of Distributions
Yue Wu, Shuaicheng Zhang, Wenchao Yu +5
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while mai…
Strength-Adaptive Adversarial Training
Chaojian Yu, Dawei Zhou, Li Shen +5
Adversarial training (AT) is proved to reliably improve network's robustness against adversarial data. However, current AT with a pre-specified perturbation budget has limitations…
Hiding Visual Information via Obfuscating Adversarial Perturbations
Zhigang Su, Dawei Zhou, Nannan Wangu +3
Growing leakage and misuse of visual information raise security and privacy concerns, which promotes the development of information protection. Existing adversarial perturbations-b…
Improving Adversarial Robustness via Mutual Information Estimation
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative…
Modeling Adversarial Noise for Adversarial Training
Dawei Zhou, Nannan Wang, Bo Han +1
Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks. Motivated by the fact that adve…