为复杂数字取证建立可读开放标准，提升可靠性。
Towards Open Standards for Systemic Complexity in Digital Forensics
- 提出基于前沿技术的可读取证模型架构
- 通过开放标准降低系统性错误风险
- 适合关注取证可信度的研究者与从业者
人工智能与数字取证的交叉日益复杂且广泛，各类科学和技术探究均在应用重叠的技术与方法。尽管取得显著进展，取证科学仍易出错，存在固有脆弱性。为缓解取证中的错误问题，本文识别并应对系统性复杂性，提出采用人类可读的成果物和开放标准。文中概述了基于当前最先进技术的数字取证AI模型架构。
原文摘要 · Abstract (English)
The intersection of artificial intelligence (AI) and digital forensics (DF) is becoming increasingly complex, ubiquitous, and pervasive, with overlapping techniques and technologies being adopted in all types of scientific and technical inquiry. Despite incredible advances, forensic sciences are not exempt from errors and remain vulnerable to fallibility. To mitigate the limitations of errors in DF, the systemic complexity is identified and addressed with the adoption of human-readable artifacts and open standards. A DF AI model schema based on the state of the art is outlined.
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