article

Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames

Authors:

Jamimamul Bakas,

Ruchira Naskar,

Rahul DixitAuthors Info & Claims

Multimedia Tools and Applications, Volume 78, Issue 4

Pages 4905 - 4935

https://doi.org/10.1007/s11042-018-6570-8

Published: 01 February 2019 Publication History

Abstract

With the immensely growing rate of cyber forgery today, the integrity and authenticity of digital multimedia data are highly at stake. In this work, we deal with forensic investigation of cyber forgery in digital videos. The most common types of inter-frame forgery in digital videos are frame insertion, deletion and duplication attacks. A number of significant researches have been carried out in this direction, in the past few years. In this paper, we propose a two-step forensic technique to detect frame insertion, deletion and duplication types of video forgery. In the first step, we detect outlier frames, based on Haralick coded frame correlation; and in the second step, we perform a finer degree of detection, to eliminate false positives, hence to optimize the forgery detection accuracy. Our experimental results prove that the proposed method outperforms the state---of---the---art with an average F1 score of 0.97 in terms of inter---frame video forgery detection accuracy.

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Cited By

Shelke NKasana S(2024)Multiple forgery detection in digital video with VGG-16-based deep neural network and KPCAMultimedia Tools and Applications10.1007/s11042-023-15561-083:2(5415-5435)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15561-0
Mohiuddin SMalakar SKumar MSarkar R(2023)A comprehensive survey on state-of-the-art video forgery detection techniquesMultimedia Tools and Applications10.1007/s11042-023-14870-882:22(33499-33539)Online publication date: 4-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14870-8
Bozkurt IUlutaş G(2023)Detection and localization of frame duplication using binary image templateMultimedia Tools and Applications10.1007/s11042-023-14602-y82:20(31001-31034)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14602-y
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Index Terms

Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Security and privacy

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 78, Issue 4

Feb 2019

1215 pages

ISSN:1380-7501

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Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2019

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Cited By

Shelke NKasana S(2024)Multiple forgery detection in digital video with VGG-16-based deep neural network and KPCAMultimedia Tools and Applications10.1007/s11042-023-15561-083:2(5415-5435)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15561-0
Mohiuddin SMalakar SKumar MSarkar R(2023)A comprehensive survey on state-of-the-art video forgery detection techniquesMultimedia Tools and Applications10.1007/s11042-023-14870-882:22(33499-33539)Online publication date: 4-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14870-8
Bozkurt IUlutaş G(2023)Detection and localization of frame duplication using binary image templateMultimedia Tools and Applications10.1007/s11042-023-14602-y82:20(31001-31034)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14602-y
Mohiuddin SMalakar SSarkar R(2023)An ensemble approach to detect copy-move forgery in videosMultimedia Tools and Applications10.1007/s11042-023-14554-382:16(24269-24288)Online publication date: 11-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14554-3
Singla NSingh JNagpal STokas B(2023)HEVC based tampered video database development for forensic investigationMultimedia Tools and Applications10.1007/s11042-022-14303-y82:17(25493-25526)Online publication date: 10-Jan-2023
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Panchal HShah H(2023)Multiple forgery detection in digital video based on inconsistency in video quality assessment attributesMultimedia Systems10.1007/s00530-023-01123-929:4(2439-2454)Online publication date: 9-Jun-2023
https://dl.acm.org/doi/10.1007/s00530-023-01123-9
Singla NNagpal SSingh J(2023)A two-stage forgery detection and localization framework based on feature classification and similarity metricMultimedia Systems10.1007/s00530-023-01050-929:3(1173-1185)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1007/s00530-023-01050-9
Raskar PShah S(2022)VFDHSOG: Copy-Move Video Forgery Detection Using Histogram of Second Order GradientsWireless Personal Communications: An International Journal10.1007/s11277-021-08964-5122:2(1617-1654)Online publication date: 1-Jan-2022
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Kumar VGaur M(2022)Multiple forgery detection in video using inter-frame correlation distance with dual-thresholdMultimedia Tools and Applications10.1007/s11042-022-13284-281:30(43979-43998)Online publication date: 1-Dec-2022
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Megahed AHan Q(2022)Identify videos with facial manipulations based on convolution neural network and dynamic textureMultimedia Tools and Applications10.1007/s11042-022-13102-981:30(43441-43466)Online publication date: 1-Dec-2022
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