向编辑致信:重新审视深度学习在网络犯罪学中的作用:超越概念框架的实际差距
Sandhya Anil Harihar1, Anant Sidhappa Kurhade2, Shital Yashwant Waware2
1S. B. Shirkoli Homoeopathic Medical College and Hospital, Sankeshwar, Karnataka, 591313, India.
Forensic science international. Synergy
|March 12, 2026
概括
网络法医学的深度学习显示出希望,但面临着数据质量和验证等挑战. 这些人工智能工具应该增强,而不是取代,专家判断可靠的数字调查.
科学领域:
- 网络法医科学是网络法医科学.
- 人工智能的人工智能
- 数字调查数字调查
背景情况:
- 深度学习模型越来越多地被用于网络法医.
- 当前的模型往往无法解决现实世界的利用问题.
- 关键问题包括数据质量,偏见,验证和可解释性.
研究的目的:
- 在网络法医中调查深度学习的实际局限性.
- 探索这些技术在现实世界中应用的挑战.
- 讨论需要从事件审查向实时监控进行概括的需要.
主要方法:
- 探索基于深度学习的网络法医技术的实际局限性.
- 分析与数据质量,偏见和验证相关的问题.
- 讨论可解释性和证据完整性的风险.
主要成果:
- 当前的网络法医深度学习模型存在显著的实际限制.
- 问题包括数据质量差,缺乏法医级验证,以及模型解释能力低.
- 自动比较对证据完整性构成风险.
结论:
- 深度学习工具应该支持,而不是取代网络犯罪学方面的专家判断.
- 对透明度,问责制和法律可接受性的保障至关重要.
- 应对这些挑战对于可靠的数字法医流程至关重要.
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