Research
Research Interests
- AI Cybersecurity: AI Model Watermarking, Data Boundary Assurance.
- Software Security: Vulnerability Detection, Patch Management, Automatic Program Repair.
- Biometric Security: Automatic Speech Recognition, Computer Vision and Image Processing.
- Network Security: DNS Security, Network Traffic Analysis.
Research Projects
Ph.D. Projects
Software Vulnerability Analysis and Security Patch Identification 2019-2023
Advisors: Dr. Kun Sun
Open source software (OSS) has been widely used in both free and proprietary applications. The Black Duck reports that 96% of their scanned applications contain open source components, which account for 57% of the code base on average. At the same time, vulnerabilities embedded in upstream OSS are fast propagated to the underlying applications. Also, the clone or reuse of OSS without explicit reference makes it challenging for maintainers to track and mitigate vulnerabilities. Our research develops practical techniques for detecting such vulnerabilities, which help build a more reliable and secure information system infrastructure.
- GraphSPD: graph-based security patch detection.
- PatchRNN: sequential-based security patch detection.
- PatchDB: a large-scale security patch dataset.
Securing Voice Processing Systems from Malicious Audio Attacks 2018-2023
Advisors: Dr. Kun Sun
Automatic speech recognition (ASR) systems are some of the widely-used human-computer interaction systems that provide convenient voice-controlled services to users. However, ASR systems are vulnerable to adversarial audio attacks that are performed by experienced attackers with modern signal processing techniques. Our research focuses on analyzing the potential vulnerabilities of ASR systems and designing the corresponding countermeasures against adversarial audio attacks. Currently, ASR systems identify adversarial audio (e.g., replay audio) by utilizing the methods based on the frequency spectrums. However, there still exists an arms race between attackers and defenders, with attackers developing more effective methods to evade the detection model. The frequency-based defenses may still leave potential vulnerabilities if attackers are able to generate more realistic audio by frequency compensation technique.
- ModReplay: modulated replay attacks and the mitigation methods.
- SIEVE: secure in-vehicle ASR.
ACE: acoustic compensation emulation system.
Master’s Projects
Vehicle Detection and Recognition based on Deep Neural Networks 2015-2017
Advisor: Dr. Feng Liu
In this project, we first built a vehicle detection system by transfer learning over the Faster-RCNN model. With the feature maps in VGG network and the weak labels in a pre-training extreme learning machine, we then built an adaptive clustering algorithm to classify the vehicle types in ever-changing scenarios. Finally, we designed a robust ELM classifier to identify the vehicle manufacturers and vehicle models.
3D Facial Image Recognition System based on Kinect 2014
Advisor: Dr. Feng Liu
In this project, we designed a 3D face recognition system by using Kinect as an input device. The RGB images, deep images, and facial landmarks are all collected as features with Principal Component Analysis (PCA). The final recognition system is based on SVM and written in C++ and OpenCV.
