视频帧内运动目标移除篡改检测算法

doi:10.11772/j.issn.1001-9081.2017092198

计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 879-883.DOI: 10.11772/j.issn.1001-9081.2017092198

• 虚拟现实与多媒体计算 • 上一篇下一篇

视频帧内运动目标移除篡改检测算法

尹立¹, 林新棋^1,2, 陈黎飞^1,2

1. 福建师范大学数学与信息学院, 福州 350007;
2. 福建省网络安全与密码技术重点实验室(福建师范大学), 福州 350007

收稿日期:2017-09-11 修回日期:2017-10-19 出版日期:2018-03-10 发布日期:2018-03-07
通讯作者: 林新棋
作者简介:尹立(1992-),男,安徽马鞍山人,硕士研究生,主要研究方向:机器学习、多媒体信息处理;林新棋(1972-),男,福建莆田人,副教授,博士,主要研究方向:多媒体信息处理、模式识别;陈黎飞(1972-),男,福建长乐人,教授,博士,主要研究方向:统计机器学习、数据挖掘、模式识别。
基金资助:
国家自然科学基金面上项目（61672157）；福建省高等学校科技创新团队项目（IRTSTFJ，J1917）；福建师范大学"网络与信息安全关键理论和技术"校创新团队项目（IRTL1207）。

Moving object removal forgery detection algorithm in video frame

YIN Li¹, LIN Xinqi^1,2, CHEN Lifei^1,2

1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou Fujian 350007, China;
2. Fujian Provincial Key Laboratory of Network Security and Cryptography(Fujian Normal University), Fuzhou Fujian 350007, China

Received:2017-09-11 Revised:2017-10-19 Online:2018-03-10 Published:2018-03-07
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61672157), the Fujian Provincial Higher Education Innovation Team Project (IRTSTFJ, J1917), the Fujian Normal University "Network and Information Security Key Theory and Technology" School Innovation Team Project (IRTL1207).

摘要/Abstract

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

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

中图分类号:

TP391.41

尹立, 林新棋, 陈黎飞. 视频帧内运动目标移除篡改检测算法[J]. 计算机应用, 2018, 38(3): 879-883.

YIN Li, LIN Xinqi, CHEN Lifei. Moving object removal forgery detection algorithm in video frame[J]. Journal of Computer Applications, 2018, 38(3): 879-883.

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视频帧内运动目标移除篡改检测算法

Moving object removal forgery detection algorithm in video frame

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