Video inter‐frame forgery identification based on the consistency of quotient of MSSIM

Li, Zhaohong; Zhang, Zhenzhen; Guo, Sheng; Wang, Jinwei

doi:10.1002/sec.1648

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…In [10], Zhang et al used local binary patterns (LBP) to map the video content to feature space, and reveal the inconsistency trace by building correlative relationship between LBPs of each pair of adjacent frames. Li and Zhang proposed a novel feature named QoMSSIM in [11]. As its name means, this method discovered the inconsistency trace by measuring the structure similarity between adjacent frames.…”

Section: Related Workmentioning

confidence: 99%

Frame Deletion Detection Based on Optical Flow Orientation Variation

Huo

2021

IEEE Access

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Frame deletion detection is an arrestive field for video forensics in recent years. This paper presents a frame deletion detection algorithm based on optical flow orientation variation. Optical flow field has been proved to be effective for the detection of the frame deletion operation. However, current opticalflow based methods have relatively poor detection performance for real-world videos. Our research aims to develop a new forensic method to detect frame deletion for real-world videos. The proposed algorithm firstly develops an analytical model for flow orientation variation between adjacent optical flow fields, and exhibits a novel forensic clue for frame deletion detection, under an ideal but reasonable assumption. Accordingly, an effective descriptor, namely pseudo flow orientation variation (PFOV), is created to approximate the flow orientation variation, as a discriminative description feature to capture the frame deletion trace. The Frobenius norm and a smooth function are also introduced to quantify each descriptor to form an one-dimensional time series, and further statistics technique is adopted to detect the anomalous points in the time series caused by frame deletion. In addition, to improve the descriptor performance, the Robust Principal Component Analysis (RPCA) is applied to extract moving objects from video sequences and compute the proposed descriptor on them. The tests are made on 324 real-world videos, and experiment results demonstrate that the true positive rate can reach 90.12%, meanwhile the false alarm rate is 7.71%, which indicates the superiority of the proposed method in the forensic algorithms for real-world video.

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Section: Related Workmentioning

confidence: 99%

Frame Deletion Detection Based on Optical Flow Orientation Variation

Huo

2021

IEEE Access

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“…(2013toperforminter-frameforgery classificationbasedonSVMclassifier.Limitationsofbothstudiesarethattheforgeddatabaseswere unrealisticallycreated,andbothstudiesfocusedpurelyonclassificationwithoutlocalizationanalysis. Li, Zhang, Guo, and Wang (2016) utilized consistency of quotient of mean structural similarity (QoMSSIM)thatisbasedonthestructuralsimilarity(SSIM)imagequalitymetric (Lowe,2004)to performinter-frameforgerydetection. Zheng,Sun,andShi(2014)demonstratedacomputationallyefficient,threshold-lessBlock-wise BrightnessVarianceDescriptor(BBVD)algorithmthatreliesonabrightnessvariancedescriptor featuredenotedas R BVD todetectinter-frameforgery.Huang,Zhang,andThing(2017)demonstrated anovelMulti-LevelSubtraction(MLS)frameworkforinter-framevideoforgerydetection.…”

Section: Coding-based Detection Techniquesmentioning

confidence: 99%

A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification

Huang

Lee

Thing

2020

International Journal of Digital Crime and Forensics

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Video forgery has been increasing over the years due to the wide accessibility of sophisticated video editing software. A highly accurate and automated video forgery detection system will therefore be vitally important in ensuring the authenticity of forensic video evidences. This article proposes a novel Triangular Polarity Feature Classification (TPFC) video forgery detection framework for video frame insertion and deletion forgeries. The TPFC framework has high precision and recall rates with a simple and threshold-less algorithm designed for real-world applications. System robustness evaluations based on cross validation and different database recording conditions were also performed and validated. Evaluation on the performance of the TPFC framework demonstrated the efficacy of the proposed framework by achieving a recall rate of up to 98.26% and precision rate of up to 95.76%, as well as high localization accuracy on detected forged videos. The TPFC framework is further demonstrated to be capable of outperforming other modern video forgery detection techniques available today.

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“…Mean peak signal-to-noise ratio (MPSNR) and mean structural similarity index (MSSIM) [19] are commonly used to quantitatively evaluate the imperceptibility of video watermarking algorithms. If the number of watermarked frames is K and the size of video frames is M × N, the definition of MPSNR I MPSNR is given as:…”

Section: Imperceptibilitymentioning

confidence: 99%

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

Wang

Zhou

2018

Applied Sciences

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With the development and popularization of the Internet and the rise of various live broadcast platforms, digital videos have penetrated into all aspects of people’s life. At the same time, all kinds of pirated videos are also flooding the Internet, which seriously infringe the rights and interests of video copyright owners and hinder the healthy development of the video industry. Therefore, robust video watermarking algorithms for copyright protection have emerged as these times require. In this paper, we review robust video watermarking algorithms for copyright protection based on original videos and compressed videos. Basic models and properties of video watermarking algorithms are described, and the evaluation indexes corresponding to each property are also introduced. To help researchers understand various existing robust watermarking algorithms quickly, some basic information and the quantitative estimation of several performances are analyzed and compared. Finally, we discuss the challenges in the research of robust video watermarking algorithms, and give possible development directions for the future.

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Video inter‐frame forgery identification based on the consistency of quotient of MSSIM

Cited by 18 publications

References 12 publications

Frame Deletion Detection Based on Optical Flow Orientation Variation

Frame Deletion Detection Based on Optical Flow Orientation Variation

A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

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