用AI提取数字证据时如何保证可靠？这篇论文给出了可审计的解决方案。
Evaluating the Reliability of Digital Forensic Evidence Discovered by Large Language Model: A Case Study
- 构建基于LLM的自动化取证框架，结合知识图谱验证证据
- 在13GB数据上实现95%以上证据提取准确率，确保证据链完整
- 适合需要可信AI取证的司法机构与安全团队使用
随着大型语言模型（LLMs）越来越多地应用于数字取证，其识别出的数字证据的可靠性引发关注。本文提出一个结构化框架，实现取证内容的自动化提取，通过LLM分析优化数据，并利用数字取证知识图谱（DFKG）进行结果验证。该框架在包含61个应用、2,864个数据库和5,870张表的13 GB取证图像数据集上进行了评估，通过确定性唯一标识符（UIDs）和取证交叉验证，确保了取证内容的可追溯性和证据一致性。案例研究显示，该框架在证据提取中达到95%以上的准确率，有效支持证据保管链完整性，并在取证关系上下文中表现出强一致性。结果表明，该方法能提升证据可靠性、减少分类错误，为人工智能辅助数字取证建立法律可接受的可审计范式。
原文摘要 · Abstract (English)
The growing reliance on AI-identified digital evidence raises significant concerns about its reliability, particularly as large language models (LLMs) are increasingly integrated into forensic investigations. This paper proposes a structured framework that automates forensic artifact extraction, refines data through LLM-driven analysis, and validates results using a Digital Forensic Knowledge Graph (DFKG). Evaluated on a 13 GB forensic image dataset containing 61 applications, 2,864 databases, and 5,870 tables, the framework ensures artifact traceability and evidentiary consistency through deterministic Unique Identifiers (UIDs) and forensic cross-referencing. We propose this methodology to address challenges in ensuring the credibility and forensic integrity of AI-identified evidence, reducing classification errors, and advancing scalable, auditable methodologies. A comprehensive case study on this dataset demonstrates the framework's effectiveness, achieving over 95 percent accuracy in artifact extraction, strong support of chain-of-custody adherence, and robust contextual consistency in forensic relationships. Key results validate the framework's ability to enhance reliability, reduce errors, and establish a legally sound paradigm for AI-assisted digital forensics.
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