基于深度学习的视频篡改检测方法  

Video Tampering Detection Method Based on Deep Learning

作　　者：陈雪艳[1,2] 梁大超 宋启东 王洪泰 CHEN Xueyan;LIANG Dachao;SONG Qidong;WANG Hongtai(College of Engineering,Inner Mongolia Minzu University,Tongliao 028043,China;Key Laboratory of Intelligent Manufacturing Technology,Inner Mongolia Minzu University,Tongliao 028043,China;Inner Mongolia Tongyuan New Energy Technology Co.,Ltd,Tongliao 028000,China)

机构地区：[1]内蒙古民族大学工学院,内蒙古通辽028043 [2]内蒙古民族大学智能制造技术重点实验室,内蒙古通辽028043 [3]内蒙古通元新能源科技有限公司,内蒙古通辽028000

出　　处：《内蒙古民族大学学报（自然科学版）》2024年第4期52-55,共4页Journal of Inner Mongolia Minzu University：Natural Sciences Edition

基　　金：国家自然科学基金项目(61440041);内蒙古自治区青年科技英才项目(NJYT22051);内蒙古自治区高等学校科学技术重点项目(NJZZ19144);内蒙古自治区直属高校基本科研业务费项目(GXKY22045)。

摘　　要：随着新一代信息技术的迅猛发展,视频图像处理的多媒体技术应用越发广泛,数字视频的真实性面临巨大挑战。对视频篡改问题进行了研究,提出了一种基于深度学习的检测方法,即将卷积神经网络(CNN)和长短期记忆网络(LSTM)相结合,融入哈希(Hash)算法,利用卷积神经网络提取特征值,输入至长短期记忆网络,通过全连接层Softmax提取时空特征构建了CNNH-LSTM融合网络模型。将该网络模型应用于直播类视频篡改检测分类,实验结果表明,该模型具有良好的特征提取能力、泛化能力,并具有较好的区分性,为视频篡改检测提供了一种新思路。With the rapid development of the new generation of information technology,the application of multi-media technology for video or image processing is becoming more and more widespread,and the authenticity of digi-tal videos faces huge challenges.In this paper,a video tampering detection algorithm based on deep learning is pro-posed:the Convolutional Neural Network(CNN)is combined with the Long Short-Term Memory(LSTM)network,and the Hash algorithm is integrated,the features extracted by the convolutional neural network are input into the long-term and short-term memory network model,and the spatial and temporal features are extracted through the fully connected layer Softmax to construct CNNH-LSTM in the fusion model.By applying this network model to live video tampering detection and classification,the experimental results show that the model has good feature extraction capability generalization capability,and good distinguishing,providing a new method for video tamper detection.

关 键 词：神经网络 哈希算法 视频篡改 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]