使用专家审查的CSAM来训练CNN及其人类学分析分析
Wojciech Oronowicz-Jaśkowiak1, Tomasz Kozłowski2, Marta Polańska3
1Faculty of Computer Science, Polish-Japanese Academy of Information Technology, Poland.
Journal of forensic and legal medicine
|November 21, 2023
概括
这项研究开发了先进的机器学习模型,使用专家注释的图像来准确识别儿童性虐待材料 (CSAM). 这些模型,特别是ResNet-s,显示出对法医分析和专家证人支持的前景.
科学领域:
- 法医科学 法医科学 法医科学
- 计算机科学 计算机科学
- 人类学是人类学.
背景情况:
- 以前用于CSAM识别的机器学习模型存在局限性,包括缺乏专家注释和使用真实儿童剥削图像.
- 现有的解决方案往往无法为分类决定提供详细的理由,这对于欧盟人工智能法案等监管合规至关重要.
研究的目的:
- 训练卷积神经网络 (CNN),使用专家标记的CSAM图像来识别关键的视觉元素进行分类.
- 评估不同神经网络架构在检测CSAM时的有效性.
- 为CSAM识别提供透明和合理的AI工具.
主要方法:
- 在四个类别的6万张图像上训练了四个神经网络架构 (MobileNet,ResNet152,xResNet152,ResNet-s):CSAM,成人性内容,非性人类图像和没有人的图像.
- 使用了由人类学和性学法医专家注释的图像.
- 采用专家知识来改善模型培训和解释.
主要成果:
- 在CSAM分类中实现了高精度,xResNet152和ResNet-s达到F1分数为0.93 (分别为92.8%和93.1%的精度).
- ResNet152和MobileNet也表现出强的表现 (F1=0.90,91.39%和F1=0.85-0.87,精度为86-87%).
- 识别了乳房,脸部和干作为分类的关键区域,支持人类学分析.
结论:
- 专家注释的数据显著提高了用于CSAM检测的机器学习模型的准确性.
- ResNet的神经网络显示出作为法医专家和人类学相关法律案件的可靠工具的潜力.
- 为法医专家,执法人员和检察官提供CSAM分类的免费应用程序.
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