Robust median filtering forensics based on the autoregressive model of median filtered residual
Robust median filtering forensics based on the autoregressive model of median filtered residual
复制标题
DOI：
--
复制
发表时间：
2012-12
期刊：
影响因子：
--
通讯作者：
Xiangui Kang;M. Stamm;Anjie Peng;K. Liu
中科院分区：
文献类型：
--
作者：
Xiangui Kang;M. Stamm;Anjie Peng;K. Liu
多媒体取证的一个重要方面是暴露图像的处理历史。中值滤波是一种流行的噪声消除和图像增强工具。也是近年来反取证中的有效工具。图像通常以压缩格式保存，例如JPEG格式。从 JPEG 压缩图像中进行中值滤波取证检测仍然具有挑战性，因为典型的滤波器特性受到 JPEG 量化和块伪影的抑制。在本文中，我们介绍了一种基于中值滤波残差自回归模型的鲁棒中值滤波检测方案。首先对测试图像应用中值滤波，初始图像和滤波后的输出图像之间的差异称为中值滤波残差（MFR）。 MFR 用作法医指纹。因此，可以减少图像边缘和纹理的干扰，这被认为是现有取证方法的限制。由于重叠窗口滤波引入了MFR像素之间的相关性，因此计算了MFR的自回归（AR）模型，并且支持向量机（SVM）使用AR系数进行分类。实验结果表明，所提出的中值滤波检测方法对于品质因数低至 30 的 JPEG 后压缩非常鲁棒。它可以很好地区分中值滤波和其他操作，例如高斯滤波、平均滤波和重新缩放，并且在尺寸为 32 × 32 的低分辨率图像上表现良好。所提出的方法不仅比现有的最先进方法获得更好的性能，而且具有非常小的维度 特征，即 10-D。
One important aspect of multimedia forensics is exposing an image's processing history. Median filtering is a popular noise removal and image enhancement tool. It is also an effective tool in anti-forensics recently. An image is usually saved in a compressed format such as the JPEG format. The forensic detection of median filtering from a JPEG compressed image remains challenging, because typical filter characteristics are suppressed by JPEG quantization and blocking artifacts. In this paper, we introduce a robust median filtering detection scheme based on the autoregressive model of median filtered residual. Median filtering is first applied on a test image and the difference between the initial image and the filtered output image is called the median filtered residual (MFR). The MFR is used as the forensic fingerprint. Thus, the interference from the image edge and texture, which is regarded as a limitation of the existing forensic methods, can be reduced. Because the overlapped window filtering introduces correlation among the pixels of MFR, an autoregressive (AR) model of the MFR is calculated and the AR coefficients are used by a support vector machine (SVM) for classification. Experimental results show that the proposed median filtering detection method is very robust to JPEG post-compression with a quality factor as low as 30. It distinguishes well between median filtering and other manipulations, such as Gaussian filtering, average filtering, and rescaling and performs well on low-resolution images of size 32 × 32. The proposed method achieves not only much better performance than the existing state-of-the-art methods, but also has very small dimension of feature, i.e., 10-D.