Lecturer | School of Artificial Intelligence, Guangzhou University | China
Yuan Rao is a dedicated researcher and lecturer specializing in media forensics and AI security. She has contributed significantly to the field of image forgery detection, focusing on self-supervised learning and domain adaptation techniques. Currently a Lecturer at the School of Artificial Intelligence, Guangzhou University, Yuan Rao combines academic excellence and practical expertise, earning recognition through various awards and high-impact publications.
Profile
Scopus
Google Scholar
Strengths for the Awards
- Outstanding Research Contributions
- Yuan Rao has published numerous high-impact research articles in leading journals and conferences such as:
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Pattern Recognition
- IEEE International Conference on Computer Vision (ICCV)
- Her work on JPEG-resistant image forgery detection and AI security vulnerabilities reflects groundbreaking innovation in media forensics.
- Leadership in Projects
- She has successfully undertaken four research projects as Principal Investigator (PI), funded by prestigious bodies such as:
- National Natural Science Foundation of China
- Guangdong Basic and Applied Research Foundation
- Science and Technology Foundation of Guangzhou
🎓 Education
- PhD in Information and Communication Engineering (2014.9 – 2021.6)
- Sun Yat-sen University
- Supervisor: Professor Ni Jiangqun
- Master’s in Communication and Information Systems (2011.9 – 2014.6)
- Jinan University
- Supervisor: Professor Junkai Huang
- Bachelor’s in Telecommunications Engineering and Management (2007.9 – 2011.6)
- Beijing University of Posts and Telecommunications
Work Experience
- Lecturer (2021.6 – Present)
- School of Artificial Intelligence, Guangzhou University
- Yuan Rao conducts research, supervises projects, and inspires students in the evolving fields of AI and media forensics.
Yuan Rao’s primary research interests focus on:
- Media Forensics: Detecting and localizing image forgeries using deep learning and self-supervised techniques.
- AI Security: Exploring vulnerabilities and robustness of AI models in practical scenarios.
🏆 Awards
- 🥇 Champion and 3rd Place in “Guangzhou Pazhou Algorithm Competition” – Algorithm Safety Track, 2023
- 🥇 First Prize in the National “Challenge Cup” Black Science and Technology Special Competition, 2023
- 🏅 6th Rank out of 1471 in “Secure AI Challenger Program: Tamper Detection for Forged Images,” 2020
📚 Publications
1. A Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images
- Authors: Y. Rao, J. Ni
- Publication Year: 2016
- Citations: 590
2. Deep Learning Local Descriptor for Image Splicing Detection and Localization
- Authors: Y. Rao, J. Ni, H. Zhao
- Publication Year: 2020
- Citations: 104
3. Multi-semantic CRF-based Attention Model for Image Forgery Detection and Localization
- Authors: Y. Rao, J. Ni, H. Xie
- Publication Year: 2021
- Citations: 53
4. Block-based Convolutional Neural Network for Image Forgery Detection
- Authors: J. Zhou, J. Ni, Y. Rao
- Publication Year: 2017
- Citations: 46
5. Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images
- Authors: Y. Rao, J. Ni
- Publication Year: 2021
- Citations: 29
6. Towards JPEG-Resistant Image Forgery Detection and Localization via Self-Supervised Domain Adaptation
- Authors: Y. Rao, J. Ni, W. Zhang, J. Huang
- Publication Year: 2022
- Citations: 13
7. High-accuracy Current Sensing Circuit with Current Compensation Technique for Buck–Boost Converter
- Authors: Y. Rao, W.L. Deng, J.K. Huang
- Publication Year: 2015
- Citations: 5
8. A Trigger-Perceivable Backdoor Attack Framework Driven by Image Steganography
- Authors: W. Tang, J. Li, Y. Rao, Z. Zhou, F. Peng
- Publication Year: 2024
- Citations: – (Recent publication, citation data unavailable)
9. Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography
- Authors: W. Tang, H. Yang, Y. Rao, Z. Zhou, F. Peng
- Publication Year: 2024
- Citations: – (Recent publication, citation data unavailable)
10. Exploring the Vulnerability of Self-supervised Monocular Depth Estimation Models
- Authors: R. Hou, K. Mo, Y. Long, N. Li, Y. Rao
- Publication Year: 2024
- Citations: – (Recent publication, citation data unavailable)
11. MKD: Mutual Knowledge Distillation for Membership Privacy Protection
- Authors: S. Huang, Z. Liu, J. Yu, Y. Tang, Z. Luo, Y. Rao
- Publication Year: 2023
- Citations: – (Recent publication, citation data unavailable)
12. Deep Multi-image Hiding with Random Key
- Authors: W. Zhang, W. Tang, Y. Rao, B. Li, J. Huang
- Publication Year: 2023
- Citations: – (Recent publication, citation data unavailable)
Research Projects
Yuan Rao has led multiple projects as Principal Investigator (PI):
- National Natural Science Foundation of China: Research on Self-supervised Transfer Learning Based Image Forgery Forensics.
- Guangdong Basic and Applied Basic Research Foundation: Image Forgery Localization Combining Self-supervised Learning and Knowledge Distillation.
- Science and Technology Foundation of Guangzhou: Verifiable Robustness of Object Detection Models Using Domain Knowledge.
- Science and Technology Foundation of Guangzhou: Short-term Heavy Precipitation Forecasting with Super-resolution Reconstruction.
📝 Conclusion
Yuan Rao is a prominent researcher and educator in media forensics and AI security. With a robust academic background, impressive research achievements, and numerous accolades, she is committed to advancing technologies that ensure the integrity and security of digital media.
