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| Traceable Evidence-Centric Generation for Digital Forensics |
| LIN Guokai1, SUN Yuanyuan1, GUO Hong2, Paerhati TULAJAING1, YANG Liang1, LIN Hongfei1 |
1. School of Computer Science and Technology, Dalian University of Technology, Dalian 116024;
2. Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shang- hai 200063 |
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Abstract To address the problems of vulnerable evidence boundaries, difficulty in preserving source and location information, and insufficient verifiable support for generated results in multi-source heterogeneous electronic evidence scenarios, a traceable evidence-centric generation method for digital forensics(TEC-Gen) is proposed. First, multi-source forensic data, including chat records, file records, call records and system logs, is parsed in a unified manner. Original records are regarded as the basic units to construct evidence tuples. Within each evidence tuple, the original record text and metadata, including source files, location information, timestamps and key entities, are jointly modeled to preserve the original evidence boundary and its location anchor. Then, candidate evidence is organized according to a case analysis query. Relevant records are retrieved while structural attributes, including source files, location information and key entities, are preserved, providing a unified evidence basis for subsequent generation and verification. In the generation stage, the candidate evidence and citation rules are jointly incorporated into the prompt template, and a large language model is required to attach source markers after key factual statements. Finally, the output citations are parsed and matched with valid original positions in the candidate evidence. Explicit back-links between generated statements and original evidence are established. Experimental results show that TEC-Gen outperforms closed-book generation method and standard retrieval-augmented generation method on different large language model backbones. Stable advantages are observed in text alignment, key fact coverage, and result traceability. Methodological support is provided for case summary generation, evidence localization, and assisted analysis in digital forensics.
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Received: 04 June 2026
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| Fund:Open Project of Shanghai Key Laboratory of Forensic Medicine and Key Laboratory of Forensic Science of Ministry of Justice(No.KF202623), National Natural Science Foundation of China(No.62576073), National Key Research and Development Program of China(No.2022YFC3301801) |
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Corresponding Authors:
SUN Yuanyuan, Ph.D., professor. Her research interests include natural language processing, smart justice, deep neural networks, and complex network theory and applications.
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About author:: LIN Guokai, Ph.D. candidate. His research interests include digital forensics, information retrieval, data mining and natural language processing.
GUO Hong, Master, senior engineer. Her research interests include forensic examination of audio-visual materials, digital evidence forensics, and intellectual property identification.
Paerhati TULAJIANG, Ph.D. candidate. His research interests include information retrieval and natural language processing.
YANG Liang, Ph.D., associate professor. His research interests include natural language processing, sentiment analysis and large language models.
LIN Hongfei, Ph.D., professor. His research interests include natural language processing, affective computing and social computing. |
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2026, Vol. 39 
