基于Inception-V3网络的双阶段数字视频篡改检测算法

为了克服现有数字视频取证算法识别准确率低、定位能力差等缺点，提出一种具有高识别率且定位准确的基于Inception-V3网络的二级分类取证算法.在第一级分类器中提出简单的阈值判断方法来区分原始和篡改视频，第二级分类器将采用Inception-V3网络的稠密卷积核结构来自动提取篡改视频帧的高维多尺度特征.高维多尺度特征有助于提升篡改视频帧的识别率.实验结果表明，本文提出的算法不仅能准确地检测出篡改视频，还能从篡改视频中精确定位出篡改帧.

A Two-stage Algorithm for Video Forgery Detection Based on Inception-V3 Network

In order to overcome the shortcomings (e.g. low recognition accuracy and poor localization ability) of existing video forensics techniques, a two-stage algorithm is proposed based on Inception-V3 network, whose advantage lies in the fact that it can accurately identify a forged video and locate its forged frames. After an extensive research, it is found that the average value of all the pixels in a pristine video sequence is always larger than its forged one after the video sequence is processed via several operations such as high-pass filtering and convolution. To this end, in the first stage, a simple algorithm is proposed in which a predefined threshold is employed to distinguish a forged video and a pristine video. Considering the fact that features of each frame need to be extracted manually in existing algorithms, the dense convolution kernel structure of Inception-V3 network is adopted in the second stage to automatically extract high dimensional and multi-scale features of each forged frame. Inception-V3 network can accurately locate forged frames in a forged video since dimensional and multi-scale features can more adequately express the input information. The experiments show that the proposed method performs very well both in forged video identification and forged frame localization.