I have a broad research insterest in adversarial machine learning and system security:
Adversarial Machine Learning
Model Protection in Machine Learning as a Service (MLaaS)
Privacy-preserving Machine Learning
Deep Learning for Improving Security
Anomaly Detection with Machine Learning
Machine Learning System Design for Security-critical Applications
Security System Modeling and Evaluation
Modeling and Evaluating Cache Side-channel Attacks
Attacking and Protecting Data Privacy in Edge-Cloud Collaborative Inference Systems Zecheng He, Tianwei Zhang and Ruby B. Lee
IEEE Internet of Things Journal, 2020, accepted, to appear (Journal, IF=9.515)
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
Qingsong Yao, Zecheng He, Hu Han and S. Kevin Zhou
MICCAI 2020, accepted, to appear
Model Inversion Attacks against Collaborative Inference Zecheng He, Tianwei Zhang and Ruby B. Lee
Annual Computer Security Applications Conference (ACSAC'19) (accept rate 22%)
Sensitive-Sample Fingerprinting of Deep Neural Networks Zecheng He, Tianwei Zhang and Ruby B. Lee
IEEE Conference on Computer Vision and Pattern Recognition (CVPR'19), 2019
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning Zecheng He, Aswin Raghavan, Guangyuan Hu, Sek Chai and Ruby B. Lee
How Secure Is Your Cache Against Side-channel Attacks? Zecheng He, and Ruby B. Lee
IEEE/ACM International Symposium on Microarchitecture (MICRO'17), 2017 (accept rate 18%)
Cross-Scale Color Image Restoration Under High Density Salt-and-Pepper Noise Zecheng He, Ketan Tang and Lu Fang
IEEE International Conference on Image Processing (ICIP'17), 2017
Machine Learning Based DDoS Attack Detection from Source Side in Cloud Zecheng He, Tianwei Zhang, and Ruby B. Lee
IEEE International Conference on Cyber Security and Cloud Computing, 2017
New Models for Understanding and Reasoning about Speculative Execution Attacks Zecheng He, Guangyuan Hu, and Ruby B. Lee
Smartphone Impostor Detection with Built-in Sensors and Deep Learning
Guangyuan Hu, Zecheng He and Ruby B. Lee
VerIDeep: Verifying Integrity of Deep Neural Networks through Sensitive-Sample Fingerprinting Zecheng He, Tianwei Zhang and Ruby B. Lee
Privacy-preserving Machine Learning through Data Obfuscation
Tianwei Zhang, Zecheng He and Ruby B. Lee
Research Intern, Google
Multimodal dialogue team. May 2020 - Aug 2020, Mountain View, CA
Multimodal UI embedding.
Software Engineer Intern, Facebook
Machine learning track, Core ML team in Business Integrity. May 2019 - Aug 2019, Menlo Park, CA
Detect policy-violating ads through machine learning.
Evaluate the proposed models on BI top-level metrics. Work has been adopted across teams.
Research Intern, SRI International
Jun 2017 - Sep 2017, Princeton, NJ
Enhancing deep temporal model with statistical test for processor anomaly detection in power-grid systems.
Real-time and high-reliable controller abnormal behavior detection system design.
Research Intern, Huawei Technologies R&D
Jun 2016 - Sep 2016, Bridgewater, NJ
Adaptive-depth convolutional neural network (CNN) for image style transfer.