逆重心密度的半监督学习在视频篡改检测的应用

Video Tamper Detection Based on Inverse Gravity Density Semi-Supervised Learning

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.