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A Median Filtering Forensics Approach Based on Machine Learning

机译：基于机器学习的中值过滤法学方法

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摘要

Today manipulation of digital images has become easy due to powerful computers, advanced photo-editing software and high resolution capturing devices. Verifying the integrity of images without extra prior knowledge of the image content is an important research field. Since some general post-operations, like widely used median filtering, can affect the reliability of forensic methods in various ways, it is also significant to detect them. Current image median filtering forensics algorithms mainly extract features manually. In this paper, we present a new image forgery detection method based on machine learning, which utilizes a convolutional neural networks (CNN) to automatically learn hierarchical representations from the input images. A modified CNN architecture is specifically designed to identify traces left by the manipulation. The experimental results on several public datasets show that the proposed CNN based model outperforms some state-of-the-art methods.
机译：今天，由于强大的计算机，高级照片编辑软件和高分辨率捕获设备，数字图像的操纵变得容易。在没有额外的图像内容的情况下验证图像的完整性是一个重要的研究领域。由于一些通用后操作，如广泛使用的中值滤波，可以以各种方式影响法医方法的可靠性，检测它们也很重要。当前图像中值滤波取证算法主要提取手动提取功能。在本文中，我们提出了一种基于机器学习的新图像伪造检测方法，它利用卷积神经网络（CNN）来自动学习来自输入图像的分层表示。修改后的CNN架构专门设计用于识别由操作留下的痕迹。几个公共数据集的实验结果表明，所提出的基于CNN的模型优于一些最先进的方法。

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