Attacks, Defenses and Testing for Deep Learning

Jinyin Chen received BS and Ph.D. degrees from Zhejiang University of Technology, Hangzhou, China, in 2004 and 2009, respectively. She is currently a professor and doctoral supervisor at the Institute of Cyberspace Security, Zhejiang University of Technology. Her works of the past five years are aimed at serving the security needs of intelligent applications such as national defense and public security. She has published more than 60 papers in ICSE、USENIX、ACL、ECCV、IJCAI、IEEE TDSC、IEEE TKDE、IEEE TSMC、IEEE TCAS、IEEE TNSE、IEEE TCSS、IEEE TCCN、Information Sciences、Computers and Security. More than 20 papers have been published in domestic first-level Chinese journals such as the Journal of Automation, Journal of Software, Journal of Electronics, Journal of Communication, Journal of Information Security, and Computer Research and Development. More than 200 related patents have been applied and more than 90 related invention patents have been authorized. Several platforms/tools have been developed, such as network intelligent model security intelligent immune prototype system, intelligent system data and algorithm security detection platform, intelligent system security analysis and testing and reinforcement platform. Close cooperation has been established with the Academy of Military Sciences, the Third Research Institute of Public Security, the National Network Information Office, and Huawei Singapore Research Institute. Some systems have been deployed and applied on relevant practical platforms.

Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm.-  Feature Transfer Based Stealthy Poisoning Attack for DNNs.- Adversarial Attacks on GNN Based Vertical Federated Learning.- A Novel DNN Object Contour Attack on Image Recognition.-  Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning.- Targeted Label Adversarial Attack on Graph Embedding.- Backdoor Attack on Dynamic Link Prediction.- Attention Mechanism based Adversarial Attack against DRL.- Characterizing Adversarial Examples via Local Gradient Checking.- A Novel Adversarial Defense by Refocusing on Critical Areas.-  Neuron-level Inverse Perturbation Against Adversarial Attacks.-  Adaptive Channel Transformation-based Detector for Adversarial Attacks.-  Defense Against Free-rider Attack From the Weight Evolving Frequency.-  An Effective Model Copyright Protection for Federated Learning.-  Guard the vertical federated graph learning from Property Inference Attack.- Using Adversarial Examples to Against Backdoor Attack in FL.- Evaluating the Adversarial Robustness of Deep Model by Decision Boundaries.-  Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space.- Interpretable White-Box Fairness Testing through Biased Neuron Identification.-  A Deep Learning Framework for Dynamic Network Link Prediction.