视频帧内运动目标移除篡改检测算法
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摘要
针对数字视频帧内对象被移除的篡改操作,提出了一种基于主成分分析(PCA)的篡改检测算法。首先对待测视频帧与基准帧相减得到的差异帧使用稀疏表示方法进行去噪,降低噪声对随后特征提取的干扰;其次将去噪后的视频帧进行非重叠分块,利用主成分分析提取像素点的特征并构造特征向量空间;然后使用k-means算法对特征向量空间进行分类,并将分类结果用二值矩阵表示;最后对二值矩阵进行图像形态学操作得到最终检测结果。实验结果表明所提算法的检测性能指标精确度达到91%、准确度达到100%、F1值达到95.3%,比基于压缩感知的视频篡改检测算法在性能指标上有一定程度的提高。实验证明,对于背景静止的视频,该算法能够检测出帧内运动目标被删除的篡改操作,而且对有损压缩视频具有很好的鲁棒性。
Aiming at the tampering operation on digital video intra-frame objects, a tamper detection algorithm based on Principal Component Analysis(PCA) was proposed. Firstly, the difference frame obtained by subtracting the detected video frame from the reference frame was denoised by sparse representation method, which reduced the interference of the noise to subsequent feature extraction. Secondly, the denoised video frame was divided into non-overlapping blocks, the pixel features were extracted by PCA to construct eigenvector space. Then, k-means algorithm was used to classify the eigenvector space,and the classification result was expressed by a binary matrix. Finally, the binary morphological image was operated by image morphological operation to obtain the final detection result. The experimental results show that by using the proposed algorithm, the precision and recall are 91% and 100% respectively, and the F1 value is 95. 3%, which are better than those the video forgery detection algorithm based on compression perception to some extent. Experimental results show that for the background still video, the proposed algorithm can not only detect the tampering operation to the moving objects in the frame,but also has good robustness to lossy compressed video.
Aiming at the tampering operation on digital video intra-frame objects, a tamper detection algorithm based on Principal Component Analysis(PCA) was proposed. Firstly, the difference frame obtained by subtracting the detected video frame from the reference frame was denoised by sparse representation method, which reduced the interference of the noise to subsequent feature extraction. Secondly, the denoised video frame was divided into non-overlapping blocks, the pixel features were extracted by PCA to construct eigenvector space. Then, k-means algorithm was used to classify the eigenvector space,and the classification result was expressed by a binary matrix. Finally, the binary morphological image was operated by image morphological operation to obtain the final detection result. The experimental results show that by using the proposed algorithm, the precision and recall are 91% and 100% respectively, and the F1 value is 95. 3%, which are better than those the video forgery detection algorithm based on compression perception to some extent. Experimental results show that for the background still video, the proposed algorithm can not only detect the tampering operation to the moving objects in the frame,but also has good robustness to lossy compressed video.
引文
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[15]GONZALEZ R C,WOODS R E.数字图像处理[M].阮秋琦,阮宇智,译.第3版.北京:电子工业出版社,2011:48-49.(GONZALEZ R C,WOODS R E.Digital Image Processing[M].RUAN Q Q,RUAN Y Z,translated.3rd ed.Beijing:Publishing House of Electronics Industry,2011:48-49.)
[2]ZHANG J,SU Y,ZHANG M.Exposing digital video forgery by ghost shadow artifact[C]//Mi For'09:Proceedings of the 1st ACM Workshop on Multimedia in Forensics.New York:ACM,2009:49-54.
[3]CHEN R,YANG G,ZHU N.Detection of object-based manipulation by the statistical features of object contour[J].Forensic Science International,2014,236(3):164-169.
[4]LI L,WANG X,ZHANG W,et al.Detecting removed object from video with stationary background[C]//IWDW'12:Proceedings of the 11th International Conference on Digital Forensics and Watermarking.Berlin:Springer,2012:242-252.
[5]SU L,HUANG T,YANG J.A video forgery detection algorithm based on compressive sensing[J].Multimedia Tools and Applications,2015,74(17):6641-6656.
[6]PANDEY R C,SINGH S K,SHUKLA K K.A passive forensic method for video:exposing dynamic object removal and frame duplication in the digital video using sensor noise features[J].Journal of Intelligent and Fuzzy Systems,2017,32(5):3339-3353.
[7]CHEN S,TAN S,LI B,et al.Automatic detection of object-based forgery in advanced video[J].IEEE Transactions on Circuits and Systems for Video Technology,2016,26(11):2138-2151.
[8]BAGIWA M A,WAHAB A W A,IDRIS M Y I,et al.Digital video inpainting detection using correlation of hessian matrix[J].Malaysian Journal of Computer Science,2016,29(3):179-195.
[9]SAXENA S,SUBRAMANYAM A V,RAVI H.Video inpainting detection and localization using inconsistencies in optical flow[C]//TENCON 2016:Proceedings of the 2016 IEEE Region 10 Conference.Piscataway,NJ:IEEE,2016:1361-1365.
[10]SU Y,ZHANG J,HAN Y,et al.Exposing digital video logo-removal forgery by inconsistency of blur[J].International Journal of Pattern Recognition and Artificial Intelligence,2010,24(7):1027-1046.
[11]GRUNDLAND M,DODGSON N A.Decolorize:fast,contrast enhancing,color to grayscale conversion[J].Pattern Recognition,2007,40(11):2891-2896.
[12]BERGEAUD F,MALLAT S.Matching pursuit of images[C]//Proceedings of the 1995 International Conference on Image Processing.Washington,DC:IEEE Computer Society,1995,1:53-56.
[13]AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[14]周志华.机器学习[M].北京:清华大学出版社,2016:230-232.(ZHOU Z H.Machine Learning[M].Beijing:Tsinghua University Press,2016:230-232.)
[15]GONZALEZ R C,WOODS R E.数字图像处理[M].阮秋琦,阮宇智,译.第3版.北京:电子工业出版社,2011:48-49.(GONZALEZ R C,WOODS R E.Digital Image Processing[M].RUAN Q Q,RUAN Y Z,translated.3rd ed.Beijing:Publishing House of Electronics Industry,2011:48-49.)