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A Median Filtering Forensics Approach Based on Machine Learning

机译：基于机器学习的中值滤波取证方法

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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：如今，由于功能强大的计算机，先进的照片编辑软件和高分辨率捕获设备，对数字图像的操作已变得非常容易。在没有额外的图像内容先验知识的情况下验证图像的完整性是一个重要的研究领域。由于某些常规的后期操作（如广泛使用的中值过滤）会以各种方式影响取证方法的可靠性，因此检测它们也很重要。当前的图像中值滤波取证算法主要是手动提取特征。在本文中，我们提出了一种基于机器学习的图像伪造检测新方法，该方法利用卷积神经网络（CNN）从输入图像中自动学习层次表示。经过修改的CNN架构经过专门设计，可识别操作留下的痕迹。在几个公共数据集上的实验结果表明，所提出的基于CNN的模型优于某些最新方法。

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