7 papers
Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition
Yuchao Hou, Zixuan Zhang, Jie Wang +9
As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target recognition facilitates intelligent perce…
Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systems
Zhongliang Guo, Yifei Qian, Yanli Li +6
Adversarial attacks against computer vision systems have emerged as a critical research area that challenges the fundamental assumptions about neural network robustness and securit…
Towards more transferable adversarial attack in black-box manner
Chun Tong Lei, Zhongliang Guo, Hon Chung Lee +2
Adversarial attacks have become a well-explored domain, frequently serving as evaluation baselines for model robustness. Among these, black-box attacks based on transferability hav…
My Face Is Mine, Not Yours: Facial Protection Against Diffusion Model Face Swapping
Hon Ming Yam, Zhongliang Guo, Chun Pong Lau
The proliferation of diffusion-based deepfake technologies poses significant risks for unauthorized and unethical facial image manipulation. While traditional countermeasures have…
T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting
Yifei Qian, Zhongliang Guo, Bowen Deng +5
Zero-shot object counting aims to count instances of arbitrary object categories specified by text descriptions. Existing methods typically rely on vision-language models like CLIP…
MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks
Xinxin Liu, Zhongliang Guo, Siyuan Huang +1
Neural networks have achieved remarkable performance across a wide range of tasks, yet they remain susceptible to adversarial perturbations, which pose significant risks in safety-…