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Editor's Note: In the wake of the digital revolution, electronic evidence has emerged as the "king of evidence" in judicial practice, while the deep integration of artificial intelligence is propelling legal operations into a new era of intelligent transformation. To align with the national call for strengthening the convergence of information technology and legal studies, Renmin University of China School of Law is officially launching the fourth session of its "Advanced Training Workshop on Electronic Evidence and Intelligent Case Handling" (2025).


This session of the training program is hosted by the Law School of Renmin University of China, with co-organization from Beijing Xinglai Law Firm. The course brings together leading experts from both theoretical and practical fields, focusing on cutting-edge topics such as electronic evidence rules, big data analytics, blockchain-based evidence preservation, and AI-driven evidence review. Through case studies and hands-on exercises, it empowers legal professionals to master the core skills needed for intelligent case management. The second-phase course will be taught by Dr. Zou Jinpei, Associate Professor in the Department of Computer Science at the University of Hong Kong, on "Machine Learning for Fraud Detection—A Case Study of Bitcoin."

More course details will be released soon—stay tuned!

Machine learning for fraud detection — Bitcoin Case Study

Zou Jinpei

Dr. Chow, Kam Pui

Zou Jinpei, Associate Professor in the Department of Computer Science at the University of Hong Kong.

Preview of course content

How can you develop a keen eye to spot financial fraud amid the rampant wave of scams? And in the era of blockchain-powered super apps, how do investigators separate fact from fiction when tackling cryptocurrency-related cases?

Professor Zou Jinpei kicked off the session with "Machine Learning for Fraud Detection—A Case Study on Bitcoin," focusing on cutting-edge applications of machine learning in Bitcoin fraud cases. From a technology-law interdisciplinary perspective, he shared foundational knowledge on financial fraud analysis, demonstrated how machine-learning-based fraud detection models work and their effectiveness, and then delved into the latest research findings on leveraging machine learning for cryptocurrency case investigations—using credit card fraud as a practical example for discussion and exchange.

The course will directly address the pain points in judicial practice, equipping participants with a technical framework for fraud detection, outlining the logic of judicial adaptation, and exploring future trends—laying the foundation for interdisciplinary skills needed to tackle novel crime investigations and electronic evidence reviews in the digital age.


Preview of selected course materials

The speaker provides a detailed introduction.

Zou Jinpei holds a Master’s and PhD from the University of California, Santa Barbara, and is an Associate Professor in the Department of Computer Science at the University of Hong Kong. He also serves as the Director of the Master of Science program and oversees the Master of Data Science program.

After completing his PhD in the United States, Professor Zou embarked on his academic career at the Department of Computer Science at the University of Hong Kong. His pioneering contribution came in co-founding HARNET—the first academic and research network among Hong Kong’s tertiary institutions. In his early research years, Professor Zou successfully applied expert system technology to develop innovative solutions, including an employee scheduling system for a local airline, a garbage collection vehicle dispatch system for the Regional Services Department, and even a cutting-edge fault diagnosis system for air-conditioning units.

Between 1994 and 1997, Professor Zou, along with several experts and a team of software engineers, collaborated to develop a search engine for Hong Kong Telecom’s “108 Directory Assistance” system, leveraging cutting-edge in-memory database and distributed computing technologies of the time.

Since 1997, Professor Zou has served as the Director of the Center for Information Security and Cryptography Research (CISC) at the University of Hong Kong, focusing on the fields of digital forensics and information security.

From 1997 to 2002, he managed several cryptographic system development projects, including the Strong Cryptography Library (SCL), the Strong Cryptographic Infrastructure for E-commerce, and secure electronic document storage.

In recent years, Professor Zou has shifted his research interests toward computer forensics and digital investigations, with areas of focus including computer forensics, cryptography, computer security, network monitoring, and privacy. He currently serves as the Chair of IFIP’s Digital Forensics WG11.9, Chairman of the Information Security and Forensics Society (ISFS), a member of the IT Division of the Hong Kong Institution of Engineers, a board member of the Hong Kong Association of Forensic Sciences, and a committee member of the International Symposium on Computer Forensics (SADFE). He is also the chief architect of the computer forensics tool "Shu Jian" (Digital Evidence Search Kit, or DESK) and was awarded the Knowledge Exchange Award by the Faculty of Engineering at the University of Hong Kong in 2003.

In addition to developing forensic tools, Professor Zou is also deeply committed to digital forensics and investigative research, covering areas such as live system analysis, Bayesian-model-based digital evidence analysis, digital crime scene reconstruction, and software vulnerability assessment. He has conducted in-depth studies on buffer overflow attack techniques and their defenses. Professor Zou has published numerous research papers on these topics at both local and international conferences and in academic journals—two of which even earned him the prestigious Best Paper Award at the renowned Digital Forensics Research Conference in the U.S. in 2008 and 2011. Moreover, his paper titled "The Rules of Time on NTFS File System" has been repeatedly submitted to Hong Kong courts as supporting documentation for expert reports.

Since 2004, Professor Zou has been consistently invited to assist courts and Hong Kong law enforcement agencies as a computer forensics expert, providing lawyers with expert advice on the interpretation and understanding of digital evidence in both criminal and civil proceedings in Hong Kong. In criminal cases, his expertise has been applied to areas such as child pornography, software copyright infringement, email analysis, and online fraud. In civil litigation, his work has included activities like analyzing event log timestamps and conducting internal investigations into cases involving internal theft. Additionally, in the realm of intellectual property, he has supported Hong Kong Special Administrative Region Customs in conducting digital forensic analyses for cases related to software copying and adaptation. He has also provided digital investigation and forensic services to numerous companies facing potential risks of internal IP theft—ranging from software piracy to the unauthorized replication of CAD designs—and has even assisted Hong Kong police in probing cryptocurrency-related fraud cases.

Since 2020, Professor Zou has been focusing on analyzing cases involving Bitcoin and the Telegram platform. He has conducted in-depth analyses of hardware Bitcoin wallets, Bitcoin mixers, and Bitcoin double-spending schemes, while also meticulously investigating Telegram’s private groups and channels. Based on this work, he has prepared expert reports and provided professional insights.

For more information about Professor Zou's personal profile and research achievements, please visit the link below:

  • https://i.cs.hku.hk/~chow/

  • https://www.cs.hku.hk/index.php/people/academic-staff/chow

  • https://www.ecom-icom.hku.hk/Instructors/chow-k-p


Course Assistant Lecturer

Dr. Qin Shengzhi, an expert in digital forensics at Dataport Technology Ltd., is also a visiting lecturer for the Electronic Investigation and Forensics Course at the University of Hong Kong.

He holds a PhD in Computer Science from the Faculty of Engineering at the University of Hong Kong and is currently dedicated to advancing electronic forensics education. His research focuses on digital evidence investigation, IoT security, and the application of artificial intelligence in security data analysis.

Dr. Qin has published several research papers at the International Conference on Digital Forensics (IFIP) and serves as a reviewer for the International Journal of Critical Infrastructure Protection.

His work combines cutting-edge technology with forensic methods, emphasizing cybersecurity and AI-driven innovations in data protection.


Related Reading

  • Wang, Tianyi; Liu, Ming; Cao, Wei; and Chow, K.P. (2022). Deepfake Noise Investigation and Detection. *Forensic Science International: Digital Investigation*, 42, 301395. doi:10.1016/j.fsidi.2022.301395.

  • Gong, Yanan; Chow, K.P.; Ting, Hing-Fung; and Yiu, Siu-Ming. (2022). Analyzing the Error Rates of Bitcoin Clustering Heuristics. 10.1007/978-3-031-10078-9_11.

  • Gong, Yanan; Chow, K.P.; Yiu, Siu; and Ting, Hing. (2022). Sensitivity Analysis for a Bitcoin Simulation Model. *Forensic Science International: Digital Investigation*, 43, 301449. DOI: 10.1016/j.fsidi.2022.301449.

  • Gong, Yanan; Chow, K.P.; Yiu, Siu; and Ting, Hing. (2023). Analyzing the peeling chain patterns on the Bitcoin blockchain. *Forensic Science International: Digital Investigation*, 46, 301614. DOI: 10.1016/j.fsidi.2023.301614.

  • Du, Fuqiang, Yu, Min, Li, Boquan, Chow, K.P., Jiang, Jianguo, Zhang, Yixin, Liang, Yachao, Li, Min, and Huang, Weiqing. (2024). TAENet: A Two-Branch Autoencoder Network for Interpretable Deepfake Detection. *Forensic Science International: Digital Investigation*, 50, 301808. https://doi.org/10.1016/j.fsidi.2024.301808

  • Wang, Tianyi, Liao, Xin, Chow, K.P., Lin, Xiaodong, and Wang, Yinglong. (2024). Deepfake Detection: A Comprehensive Survey from the Reliability Perspective. ACM Computing Surveys, 57. doi:10.1145/3699710.

  • Wang, Jiawen, Li, Boquan, Yu, Min, Chow, K.P., Jiang, Jianguo, Du, Fuqiang, Meng, Xiang, and Huang, Weiqing. (2025). Assessing Backdoor Risk in Deepfake Detection. 10.1007/978-3-031-71025-4_6.


We sincerely invite the legal community to register and join us!

You're also welcome to help spread the word!


Training Program Overview


01

Course Highlights

This training course covers a comprehensive "theory + technology + practical application" approach, featuring specially invited top-tier scholars and leading experts jointly delivering the lessons. The content includes:

Cutting-edge technology: Large-model applications in the legal field, standards for digital forensic examination of electronic data, and AI system cybersecurity and defense.

Case-handling Practice: New-style electronic evidence review, massive fund analysis, and handling cybercrime cases with advanced data tools

Tool practice: Intelligent evidence review, big data visualization analysis, and hands-on training with electronic evidence review software

Case-Based Teaching A comprehensive analysis of evidence and cross-examination in high-profile cases involving telecom fraud, illegal fundraising, and casino operations.


Format: Classroom lectures + Software workshops + Academic seminars + Criminal Defense Gala

Course duration: July 1–5, 2025 (5 days and 4 nights)

Course location: Within Renmin University of China


We look forward to your joining us.


02

Teaching Team

The course is primarily taught by the "Renmin University of China Electronic Evidence Research Team," which has long been dedicated to the interdisciplinary study of law and technology, seamlessly integrating theoretical insights with practical applications.


This training session will be led by Professor Liu Pinxin from the Law School of Renmin University of China, who will also serve as the faculty coordinator. Additionally, other distinguished experts and scholars from both academia and practice within the team will join as guest instructors. The organizers will carefully tailor the faculty and curriculum based on participants' professional backgrounds and research interests, ensuring top-notch training quality and service excellence.

03

A Look Back at Past Highlights

04

Registration Process and Contact Information

1. Registration

Scan the QR code below, download the registration form, fill it out, and email it to rucfaxuepeixun@ruc.edu.cn.

2. Confirm

After receiving email confirmation of successful registration, you may proceed with payment. Participants who pay using the provided payment link to enjoy the discounted rate must call Teacher Wan at (010) 62510145 to request a system update.

3. Registration Inquiries

Teacher Wan: (010) 62510145,

13301307655 (also on WeChat)

Teacher Zhang: (010) 62510145,

               13260160362

(WeChat celia1730)

Ms. Li: (010) 62510145,

18911821663 (also on WeChat)

Fax: (010) 62514365

Email: rucfaxuepeixun@ruc.edu.cn

Address: Room 611, Mingde Law Building, No. 59 Zhongguancun Street, Haidian District, Beijing

05

Fee Structure

The training fee is 7,980 yuan per person (including instructor fees, materials, certification costs, and other charges), but does not cover accommodation, meals, or transportation expenses.

Preferential policies:

Alumni of Renmin University of China (School of Law), or three or more individuals from the same organization, who register together will receive a 10% discount (CNY 7,180).

More than 10 people (including 10) from the same organization (group) who register will receive a 20% discount (CNY 6,380).

All organizations are welcome to register collectively.

Admission Brochure Link

Enrollment Prospectus for the 4th Advanced Training Workshop on Electronic Evidence and Intelligent Case Handling (2025), School of Law, Renmin University of China


In July, meet us on Renmin University's campus and let’s together unlock the doors to the future of intelligent case handling!


Source: Liu Pinxin | Mesh View Law | WeChat Official Account




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Edited and Layouted by: Wang Xin

Review: Management Committee

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