Detecting Double JPEG Compressed Color Images With the Same Quantization Matrix in Spherical Coordinates
Detecting Double JPEG Compressed Color Images With the Same Quantization Matrix in Spherical Coordinates
复制标题
球坐标下具有相同量化矩阵的双JPEG压缩彩色图像检测
DOI：
10.1109/tcsvt.2019.2922309
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发表时间：
2020-08
影响因子：
8.4
通讯作者：
Sunil Kumar Jha
中科院分区：
文献类型：
--
作者：
Jinwei Wang;Hao Wang;Jian Li;Xiangyang Luo;Yun-Qing Shi;Sunil Kumar Jha
双重JPEG压缩检测是图像取证的重要组成部分。虽然在过去的研究中已经提出了检测不同量化矩阵的双重JPEG压缩的方法，但相同量化矩阵的双重JPEG压缩的检测仍然是一个具有挑战性的问题。本文提出了一种利用球坐标系中像素点的转换误差、舍入误差和截断误差来检测彩色图像重压缩的有效方法。截断误差、舍入误差和量化误差的随机性导致随机转换误差。转换误差的像素数用于提取六维特征。基于彩色图像与三个通道中像素值的关系，将三个通道中像素的截断误差和舍入误差映射到球坐标系中。前者转换为幅度和角度提取30维特征，8维辅助特征提取的特殊点和特殊块的数量。结果，通过使用支持向量机（SVM）方法，在分类中使用了总共44维特征。之后，支持向量机递归特征消除（SVMRFE）的方法来提高分类精度。实验结果表明，该方法的性能优于现有的方法。
Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different quantization matrix, the detection of double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.
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影响因子：
10.6
作者：
R. Neelamani;R. Queiroz;Z. Fan;S. Dash;Richard Baraniuk
通讯作者：
R. Neelamani;R. Queiroz;Z. Fan;S. Dash;Richard Baraniuk
用于区分摄影图像和计算机图形的四元数小波域取证特征分析
影响因子：
3.6
作者：
Wang, Jinwei;Li, Ting;Ye, Jingyu
通讯作者：
Ye, Jingyu
影响因子：
7.3
作者：
Xiaoying Feng;I. Cox;G. Doërr
通讯作者：
Xiaoying Feng;I. Cox;G. Doërr
DOI：
10.1109/mmsp.2008.4665171
发表时间：
2008-11
期刊：
2008 IEEE 10th Workshop on Multimedia Signal Processing
影响因子：
--
作者：
Bin Li;Y. Shi;Jiwu Huang
通讯作者：
Bin Li;Y. Shi;Jiwu Huang
DOI：
10.1109/icpr.2008.4761645
发表时间：
2008-12
期刊：
2008 19th International Conference on Pattern Recognition
影响因子：
--
作者：
Chunhua Chen;Y. Shi;W. Su
通讯作者：
Chunhua Chen;Y. Shi;W. Su