Abstract: 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.

Key words: video tampering detection, sparse denoising, Principal Component Analysis (PCA), frame difference method, digital video forensics

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。

关键词: 视频篡改检测, 稀疏去噪, 主成分分析, 帧差法, 数字视频取证