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揭露反法医技术:以DCNN驱动的方法来发现对比度增强和中位过检测

Neeti Taneja¹, Gouri Sankar Mishra¹, Dinesh Bhardwaj²

¹Department of Computer Science & Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, Uttar Pradesh, India.

Journal of forensic sciences

|September 1, 2025

概括

这项研究引入了深度卷积神经网络 (DCNN),用于检测数字图像中的反法医技术,如对比度增强和中位过. 该模型达到96.42%的准确性,为法医分析提供了强大的解决方案.

科学领域:

数字法医
计算机视觉
机器学习

背景情况:

抗法医技术,如对比度增强和中位过,在数字图像法医提出了挑战.
复杂的反法医策略越来越复杂地检测图像操纵.

研究的目的:

开发一种用于检测和分类数字图像中的反法医技术的自动化方法.
为应对不断发展的反法医策略迫切需要可靠的解决方案.

主要方法:

提出了一个多类法医深度卷积神经网络 (DCNN) 架构.
该DCNN集成了特定域的特征流和剩余域预处理以突出处理工件.
用Boss数据集进行训练和测试模型.

主要成果:

拟议的DCNN模型在接受中位过和对比度增强的图像中获得了96.42%的高精度.
该模型证明了对压缩的强度,并有效地区分了各种复杂的操纵类型.
我们成功地发现了肉眼看不见的微妙操纵装置.

结论:

开发的DCNN为数字法医调查人员提供了可靠和自动化的工具.
智能预处理和针对领域的流程的整合提高了该模型对抗法医活动的有效性.
这种方法满足了数字图像法医中对精确检测技术的日益增长的需求.

关键词:

抗法医药增强对比度深度卷积神经网络中位过

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