Normalized Energy Density-Based Forensic Detection of Resampled Images
Normalized Energy Density-Based Forensic Detection of Resampled Images
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DOI：
10.1109/tmm.2012.2191946
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发表时间：
2012-06
影响因子：
7.3
通讯作者：
Xiaoying Feng;I. Cox;G. Doërr
中科院分区：
文献类型：
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作者：
Xiaoying Feng;I. Cox;G. Doërr
提出了一种检测重采样图像的新方法。该方法基于在频域图像的二阶导数中检查不同大小的窗口内存在的归一化能量密度，并利用这一特性来导出用于训练支持向量机分类器的19维特征向量。给出了BOSS数据库中7500幅原始图像的实验结果。与前人的工作比较表明，在重采样率大于1的情况下，该算法的性能与以前的工作相似，而在重采样率小于1的情况下，该算法优于以前的工作。对双线性和双三次内插进行了实验，得到了定性上相似的结果。还提供了检测带有噪声破坏和JPEG压缩的重采样图像的结果。正如预期的那样，随着噪声的增加或JPEG品质因数的下降，会观察到一些性能下降。
We propose a new method to detect resampled imagery. The method is based on examining the normalized energy density present within windows of varying size in the second derivative of the image in the frequency domain, and exploiting this characteristic to derive a 19-D feature vector that is used to train a SVM classifier. Experimental results are reported on 7500 raw images from the BOSS database. Comparison with prior work reveals that the proposed algorithm performs similarly for resampling rates greater than 1, and is superior to prior work for resampling rates less than 1. Experiments are performed for both bilinear and bicubic interpolations, and qualitatively similar results are observed for each. Results are also provided for the detection of resampled imagery with noise corruption and JPEG compression. As expected, some degradation in performance is observed as the noise increases or the JPEG quality factor declines.