A Robust Video Watermarking for Real-Time Application

Bayoudh, Ines; Jabra, Saoussen Ben; Zagrouba, Ezzeddine

doi:10.1007/978-3-319-70353-4_42

Cited by 10 publications

(8 citation statements)

References 5 publications

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“…Nguyen and Duan [ 132 ] used an even-odd quantization approach to deconstruct eight bit-level pictures from the grayscale watermark image to highly DCT coefficients, which were then used in the video luminance component. In [ 26 ], HVS features were used to transform chosen regions into a YUV space, and the Y-component was then processed to extract Krawtchouk moments using optimum commands that might improve reconstruction quality. To construct the embedding process that combines the secret key, the DCT technique is used for the resulting Krawtchouk moments.…”

Section: Video Forensics Overviewmentioning

confidence: 99%

“… The DCT-based watermarking algorithm’s resistance is inadequate in the face of powerful geometric assaults. [ 26 ] 2017 Non-Blind No Preprocessing Coefficients in the KDCT matrix of the Y component that has been chosen The Y component was used to extract Krawtchouk moments using optimum ordering to improve the reconstruction quality. [ 36 ] 2018 Blind No Preprocessing Each 2D DCT block’s AC coefficients in the Y component It takes full advantage of the Spatio-temporal properties of video sequences to decrease computing complexity and perceived redundancy.…”

Section: Video Forensics Overviewmentioning

confidence: 99%

Section: A Dct-based Watermarking Algorithmsmentioning

confidence: 99%

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A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

El-Shafai,

Fouda,

El-Rabaie

et al. 2023

Multimed Tools Appl

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Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it’s vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.

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Section: Video Forensics Overviewmentioning

confidence: 99%

Section: Video Forensics Overviewmentioning

confidence: 99%

Section: A Dct-based Watermarking Algorithmsmentioning

confidence: 99%

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A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

El-Shafai,

Fouda,

El-Rabaie

et al. 2023

Multimed Tools Appl

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show abstract

“…Nguyen and Duan [36] embedded eight bit-plane images decomposed from a grayscale watermark image into DCT coefficients with robustness, which were selected in the video luminance component using an even-odd quantization algorithm. In a previous paper [37], selected regions, based on the properties of the human visual system (HVS), were converted to the YUV space, and then the Y component was processed to extract Krawtchouk moments using optimal orders which can maximize the quality of the reconstruction. The DCT scheme is implemented to the obtained Krawtchouk moments to achieve the embedding process combining the secret key.…”

Section: Dct-based Watermarking Algorithmsmentioning

confidence: 99%

“…[34] Blind Arnold transform High-frequency coefficients of R, G, and B components [35] Blind CDMA DCT coefficients of Y component [36] Blind -Robust DCT coefficients selected in Y component [37] Non-blind -Selected coefficients in KDCT matrix of Y component [38] Semi-blind Arnold transform Low-frequency DCT coefficients of Y component [40] Blind Permuted processing AC values of selected DCT blocks in Y component [41] Blind Pseudorandom generator Y component of each I frame [43] Blind -AC coefficients of each 2D DCT block in Y component…”

Section: Ref Type Watermark Preprocessing Embedding Positionmentioning

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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