基于Inception-V3网络的双阶段数字视频篡改检测算法

翁韶伟; 彭一航; 危博; 易林; 叶武剑

doi:10.12052/gdutxb.190046

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基于Inception-V3网络的双阶段数字视频篡改检测算法

综合研究 | 更新时间：2026-05-27

- 基于Inception-V3网络的双阶段数字视频篡改检测算法
- A Two-stage Algorithm for Video Forgery Detection Based on Inception-V3 Network
- 广东工业大学学报 2019年36卷第6期页码：16-23
- 作者机构：
  
  1. 广东工业大学　信息工程学院,广东,广州,510006
  2. 广东省智能信息重点实验室,广东,深圳,518060
- 作者简介：
  
  翁韶伟(1980–)，女，副教授，博士，主要研究方向为图像可逆水印、信息隐藏和视频篡改等
  易林(1992–)，男，硕士研究生，主要研究方向为深度学习和多媒体数字取证等，E-mail：90583068@qq.com
- 基金信息：
  
  国家自然科学基金资助项目(61872095，61872128，61571139，61201393);广东省智能信息处理重点实验室、深圳市媒体信息内容安全重点实验室2018年开放基金课题(ML-2018-03);广东省信息安全技术重点实验室开放课题基金资助(2017B030314131);广州市珠江科技新星专题(2014J2200085)
- DOI：10.12052/gdutxb.190046
  中图分类号： TP391
- 纸质出版：2019
- 稿件说明：
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翁韶伟, 彭一航, 危博, 易林, 叶武剑. 基于Inception-V3网络的双阶段数字视频篡改检测算法[J]. 广东工业大学学报, 2019, 36(6): 16-23.

Weng Shao-wei, Peng Yi-hang, Wei Bo, et al. A Two-stage Algorithm for Video Forgery Detection Based on Inception-V3 Network[J]. 2019, 36(6): 16-23.
翁韶伟, 彭一航, 危博, 易林, 叶武剑. 基于Inception-V3网络的双阶段数字视频篡改检测算法[J]. 广东工业大学学报, 2019, 36(6): 16-23. DOI： 10.12052/gdutxb.190046.

Weng Shao-wei, Peng Yi-hang, Wei Bo, et al. A Two-stage Algorithm for Video Forgery Detection Based on Inception-V3 Network[J]. 2019, 36(6): 16-23. DOI： 10.12052/gdutxb.190046.

摘要

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.

Abstract

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.

关键词

Keywords

references

NA S, OH W, JEONG D. A frame-based video signature method for very quick video identification and location [J]. ETRI Journal, 2013, 35(2): 281-291.

LEE S, YOO C D. Robust video fingerprinting for content-based video identification [J]. IEEE Trans on Circuits and Systems for Video Technology, 2008, 18(7): 983-988.

CHETTY G, BISWAS M, SIGNH R. Digital video tamper detection based on multimodal fusion of residue features[C] //Fourth International Conference on Network System Security. IEEE Computer Society. Melbourne: IEEE, 2010: 606-613.

GOODWIN J, CHETTY G. Blind video tamper detection based on fusion of source features[C] //International Conference on Digital Image Computing: Techniques Applications. Noosa: IEEE, 2011: 608-613.

BESTAGINI P, MILANI S, TAGLIASACCHI M, et al. Local tampering detection in video sequences[C] //Proceedings of the IEEE 15th International Workshop on Multimedia Signal Processing. Pula: IEEE, 2013: 488–493.

LI L, WANG X, ZHANG W, et al. Detecting removed object from video with stationary background[C] //International Conference on Digital Forensics Watermarking. Shanghai: Springer, 2012: 242-252.

BIDOKHTI A, CHAEMMAGHAMI S. Detection of region copy move forgery in MPEG videos using optical flow[C] // International Symposium on Artificial Intelligence and Signal Processing. Mashhad: IEEE, 2015: 13-17.

PANDEY R C, SINGH S K, SHUKLA K K. Passive copy-move forgery detection in videos[C] //Proceedings of the International Conference on Computer and Communication Technology. Allahabad: Springer, 2014: 301–306.

YE W J, GAO H J, WENG S W, et al . A Two-sta ge effect rendering method for art font based on CGAN network [J ] . Journal of Guangdong University of Technology, 2019, 36(3): 48-54.

CHEN S, TAN S, LI B, et al . Automatic detection of object-based forgery in advanced video [J ] . IEEE Trans on Circuits and Systems for Video Technology, 2016, 26(11): 2318-2350.

YAO Y, SHI Y, WENG S, et al . Deep learning for detection of object-based forgery in advanced video [J ] . Symmetry, 2017, 10(3): 1-10.

LIU S H, HAN L, YAO H X. Video watermarking algorithm for resisting collusion attacks [J]. Journal on Communications, 2010, 31(1): 14-19.

CHRISTIAN S, VICENT V, LOFFE S, et al. Rethinking the inception architecture for computer vision[C] //IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2818-2826.

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