摘要
针对视频遭受非同源片段合成的篡改,根据其特点以及数据特征,提出一种改进的半监督学习算法,并将这种算法应用在视频篡改取证研究.首先,以每一个视频帧作为样本,提取其R、G、B分量,对这三个分量分别提取噪声,然后以这三个噪声模板的均值、方差等统计量构成样本属性.最后,用本文提出半监督学习算法对样本集聚类.实验结果表明本文提出的算法能够有效检测视频是否由非同源片段合成.
Aim at the condition that a video may be combined from non-same source segments, and according to its data characteristic, we propose an improved semi-supervised learning algorithm, and apply it to the research of detecting video's authenticity. First, use per-frame of a video as a sample, extracting their R, G, B components, and getting the noise of these three components. Then, getting these noise's mean and variance, and use it as attributes of the sample. Last, use this paper's algorithm cluster the samples. Experimental results show that this method can effectively identify whether a video is combined from non-same source segments.
出处
《计算机系统应用》
2013年第8期91-97,102,共8页
Computer Systems & Applications
基金
国家自然科学基金(61070062
61073017)
福建省高校产学合作科技重大项目(2012H6006)
福建省高校服务海西建设重点项目(2008HX200941-4-5)
福建省高等学校新世纪优秀人才支持计划(JAI1038)
关键词
半监督聚类
视频篡改
模式噪声
小波变换
semi-supervised clustering
video tamper
pattern noise
wavelet transform