Adversarial attack is aimed at fooling the target classifier with imperceptible perturbation. Adversarial examples, which are carefully crafted with a malicious purpose, can lead to erroneous predictions, resulting in catastrophic accidents. To mitigate the effects of adversarial attacks, we propose a novel purification model called CAP-GAN. CAP-GAN takes account of the idea of pixel-level and feature-level consistency to achieve reasonable purification under cycle-consistent learning. Specifically, we utilize the guided attention module and knowledge distillation to convey meaningful information to the purification model. Once a model is fully trained, inputs would be projected into the purification model and transformed into clean-like images. We vary the capacity of the adversary to argue the robustness against various types of attack strategies. On the CIFAR-10 dataset, CAP-GAN outperforms other pre-processing based defenses under both black-box and white-box settings.
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
Tsinghua University（清华大学）；Peking University（北京大学）；Beijing Institute of Technology（北京理工大学）；Aalborg University（奥尔堡大学）；Harbin Institute of Technology, Weihai（哈尔滨工业大学（威海））；Huawei Technologies（华为技术有限公司）
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
School of Cyber Science and Engineering, Southeast University, Purple Mountain Laboratories, and Jiangsu Province Engineering Research Center of Security for Ubiquitous Network（网络 ubiquity 安全江苏省工程研究中心、紫金山实验室、东南大学网络科学与工程学院）
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
Center for Data Science, Zhejiang University, Hangzhou 310000, China（数据科学中心，浙江大学，杭州）；College of Computer Science, Zhejiang University, Hangzhou 310000, China（计算机科学学院，浙江大学，杭州）
