基于冗余性的图像重采样检测

数字图像取证中，目前的重采样检测算法都是检测图像中是否存在插值过程引入的周期性，而周期性的判定一般通过在频域的幅度谱中寻找峰值来进行，进而通过峰值的位置来计算重采样因子。但是由于重采样过程中的频率混叠问题导致了重采样因子不能完全确定。针对这个问题，本文提出一种时域中计算重采样因子的方法。重采样图像中每个像素行（或列）和相邻行（或列）的冗余性大小不同，并且冗余性大小呈现出周期性的分布。通过检测此特征就可以实现对重采样的取证，并且确定重采样因子。实验显示，在未压缩的图像中算法可以正确地估计出所有重采样因子，在压缩图像中本文的算法较之前的算法也有明显的优势。

Detection of Resampling in Digital Image Based on Redundancy

In digital image forensics, most of the works perform resampling detection by detecting periodicity introduced by the interpolation process. The detection of periodicity is usually done in frequency domain where the resampling rate can be determined simultaneously. But due to frequency aliasing, the resampling rate cannot be totally determined. To solve this problem, an algorithm is proposed which can determine the resampling rate in spatial domain, based on the observation that the redundancy of the image rows (or columns) vary periodically and can be used to determine the resampling rate. As experiments show, all resampling rates can be correctly detected in uncompressed images, and in compressed images the proposed algorithm shows more robustness compared to the prior algorithm.