A dynamic video watermarking algorithm in fast motion areas in the wavelet domain

El'arbi, Maher; Koubaa, Mohamed; Charfeddine, Maha; Amar, Chokri Ben

doi:10.1007/s11042-010-0580-5

Cited by 37 publications

(7 citation statements)

References 18 publications

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“…On the basis of the JND threshold, the scrambled watermark is embedded in mid-frequency coefficients. El'Arbi et al [45] embedded different parts of the watermark into several shots of a video sequence. On the basis of the motion activity analysis, region complexity and motion information are combined to separate different regions of the original video into perceptually distinct categories.…”

Section: Dwt-based Watermarking Algorithmsmentioning

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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Section: Dwt-based Watermarking Algorithmsmentioning

confidence: 99%

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

Wang

Zhou

2018

Applied Sciences

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“…Recently, Face recognition has received a great attention in many fields from security, psychology, and image processing, to computer vision [1][2][3][4][5] because of its accuracy and low intrusiveness. It is one of the biometric techniques used for identification individuals by comparing face image with the ones stored in the database [6].…”

Section: Introductionmentioning

confidence: 99%

An enhanced Biometric-based Face Recognition System using Genetic and CRO Algorithms

Surakhi¹,

Khanafseh²,

Jaffal³

2018

Adv. sci. technol. eng. syst. j.

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Face recognition is one of the most well-known biometric methods. It is a technique used for identifying individual from his face. The recognition process takes the face and compares it with the one stored in the database for recognizing it. Many methods were proposed to achieve that. In this paper, a new technique is proposed by using meta-heuristic algorithm. The algorithm is used to search for the best point in the image to be selected as a pivot, generate a vector of extracted features that are not necessary for the recognition and may reduce accuracy of it and evaluate the weight value for each area in the face image areas. A dataset with 371 images was used for evaluation. The results were conducted and compared with the original face recognition technique. That results show that proposed idea could enhance recognition by increasing accuracy up to 20% over original face recognition technique.

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“…Indeed, our work is specially motivated by studying the effectiveness of the histogram encoding methods [23] in the person re-ID task, since they can simplify the matching process between persons, with respect to the brute force matching methods. Bag Of Statistical Sampling Analysis BossaNova (BN) [24,25] is a pooling method that demonstrated its effectiveness in the image classification field [11,26,27,28,29,30,31,32,33,34]. It consists in discretizing the patches to clusters' assignments into several Bins.…”

Section: Introductionmentioning

confidence: 99%

Person re-ID while Crossing Different Cameras: Combination of Salient-Gaussian Weighted BossaNova and Fisher Vector Encodings

Mejdoub¹,

Ksibi²,

Amar³

et al. 2017

ijacsa

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Abstract-Person re-identification (re-ID) is a challenging task in the camera surveillance field, since it addresses the problem of re-identifying people across multiple non-overlapping cameras. Most of existing approaches have been concentrated on: 1) achieving a robust and effective feature representation; and 2) enforcing discriminative metric learning to predict if two images represent the same identity. In this context, we present a new approach for person re-ID built upon multi-level descriptors. This is achieved by combining three complementary representations: salient-Gaussian Fisher Vector (SGFV) encoding method, salient-Gaussian BossaNova (SGBN) histogram encoding method and deep Convolutional Neural Network (CNN) features. The two first methods adapt the histogram encoding framework to the person re-ID task. This is achieved by integrating the pedestrian saliency map and the spatial location information, in the histogram encoding process. On one hand, human saliency is reliable and distinctive in the person re-ID task, since it can model the uniqueness of the identity. On the other hand, localizing a person in the image can effectively discard noisy background information. Finally, one of the most advanced metric learning in person re-ID: the Cross-view Quadratic Discriminant Analysis (XQDA) is applied on the top of the resulting description. The proposed method yields promising person re-ID results on two challenging image-based person re-ID benchmarks: CUHK03 and Market-1501.

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A dynamic video watermarking algorithm in fast motion areas in the wavelet domain

Cited by 37 publications

References 18 publications

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

An enhanced Biometric-based Face Recognition System using Genetic and CRO Algorithms

Person re-ID while Crossing Different Cameras: Combination of Salient-Gaussian Weighted BossaNova and Fisher Vector Encodings

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