Video Copyright Research Articles

A Study on the Guideline of Blockchain-Based Video Copyright Protection and Distribution Model

A Study on the Guideline of Blockchain-Based Video Copyright Protection and Distribution Model

A Certificate Storage Method for Confirming Video Copyright and Tracing the Source Based on Alliance Chain

In recent years, with the development and update of business models and emerging technologies, there have been more and more forms of piracy and infringement of online video works. It is difficult for the department with copyright to confirm the copyright of the work and trace the source of piracy. Alliance blockchain technology can be partially decentralized, highly controllable, and data is not public by default, providing new ideas and methods for solving the above problems. The thesis proposes a multi-factor confirmation and traceability depositing method (MCTA) for video works of alliance blockchain. First, the alliance member nodes store the signature information of the person in charge in each step in the private chain during video production; second, the smart contract pre-processes the source video, implants watermarks and calculates video fingerprints, and creates a video ID for each video; Finally, double-factor authentication for the suspected infringement of pirated video to confirm the right. Carry on simulation experiment through Truffle+MiniNet+SimBlock. The results show that the method proposed in the thesis is effective and feasible for depositing video works.

Optimization insisted watermarking model: hybrid firefly and Jaya algorithm for video copyright protection

In this digital era, illegitimate redistribution and protection of digital multimedia have to turn out to be the critical issue. Therefore, digital watermarking has been introduced for avoiding illegitimate works and also to ensure security and authentication as well. “Digital video watermarking is a method to hide some kind of data like audio, image, text into digital video sequences which is nothing but orders of successive still images.” This paper intends to formulate a novel video watermarking framework that includes three stages, such as (i) optimal video frame prediction, (ii) watermark embedding process and (iii) watermark extraction process. Very first, the optimal frames are determined using a new hybrid algorithm termed trial-based update on Jaya plus firefly (TU-JF) algorithm, in such a way that the peak signal-to-noise ratio (PSNR) should be maximal. The frames are assigned with a label of one or zero, where label one denotes the frame with better PSNR (can select for embedding process) and label zero denotes the frame with reduced PSNR (cannot be used for embedding). Consequently, a data library is formed from the obtained results, where each frame of videos is determined with their gray-level co-occurrence matrix (GLCM) features and labels (can embed or not). As in the proposed model, the optimal frame prediction is carried out using deep belief network (DBN) framework; the obtained data are then trained in the model. The optimal frames could be predicted in an efficient manner while testing. The significant contribution of this work concerns the optimization of hidden neurons in the DBN framework, which helps to enhance prediction accuracy. At last, the “watermark embedding process” and “watermark extraction process” are done by which the image could be embedded within the optimal frames.

A blind video watermarking algorithm robust to lossy video compression attacks based on generalized Newton complex map and contourlet transform

The rapid growth of fast communication networks for digital video transmission has created a need to copyright protection for these media. Digital video can be manipulated easily by users with various motivations. Compression is the most common attack that users can apply on videos in order to eliminate digital video copyright. Proposed technique in this article is specially designed for resisting against compression attacks. This method is presented a blind and robust watermarking method to copyright protection in digital video. In the proposed method, the coefficients of the contourlet transform are extracted and then encrypted watermark embedded into video with using the singular value decomposition (SVD). Embedding watermark in SVD domain increases the robustness of proposed method against attacks. In the embedding process and watermark encryption, pseudo-random numbers generated by the proposed new chaotic map, which is a generalized two-dimensional complex map based on the Newton model. The PSNR, SSIM, BER, and NCC measures examine the performance of the proposed method in terms of robustness and visual quality.

Biological Viral Infection Watermarking Architecture of MPEG/H.264/AVC/HEVC

This paper addresses the viral infectious watermarking (VIW) model using biological virus infection for a new-paradigm of video copyright protection of MPEG/H.264/AVC/HEVC. Our model aims to spread or infect the watermark to different codecs each time video contents are copied, edited, or transcoded. Thus, we regard the watermark as the infectious virus, the video content as the host, and the video codec as the contagion medium and then model pathogen, mutant, and contagion as the infectious watermark. Then, we define the techniques of viral infectious watermark generation, kernel-based VIW, and content-based VIW. Furthermore, we present a reversible VIW for fast infection in VIW model. This makes the video quality and strength be adaptively controlled in the infectious process. Experiment results verified that our VIW model can detect or recover the reversible watermark without loss in different codecs and also can maintain the quality of video content that is recovered to the same bit rate.

Geometrically robust video hashing based on ST-PCT for video copy detection

Copy videos flooding in the network infringe the video copyright and heavy the storage pressure on the video services server, which raises a huge demand for video copy detection techniques that can accurately and quickly detect the copies of video from huge video database. This paper aims to generate a video hash which has not only high discrimination but also robustness against geometrical and spatial-temporal transformations. First, we propose Spatial-Temporal Polar Cosine Transform (ST-PCT), which considers a video as a three-dimensional matrix and performs two-dimensional Polar Cosine Transforms (PCT) after performing a one-dimensional Discrete Cosine Transform (DCT) on video. This transformation can extract features of the spatial-temporal domain and has geometric invariance. Then, based on ST-PCT, we propose a geometrically robust video hashing method for video copy detection. The video features generated by ST-PCT are compressed and quantified to a compact binary hash code. Experimental results show that compared with the state-of-the-art methods, the proposed method has better robustness, higher accuracy, and faster calculation speed.

The study of security application of LOGO recognition technology in sports video

With the rapid development of information technology and network technology and the rapid popularization of computers and intelligent devices, network video media have gradually replaced traditional media and became the main carrier of information production, storage, dissemination, sharing, and use. However, digital information can be easily copied, modified, and disseminated, which leads to the problem of network video copyright theft which is very prominent. Aiming at the copyright problem of sports video, this paper proposes an idea of using LOGO to identify the copyright of sports video, which can automatically and effectively detect whether the video is genuine or not. Firstly, encrypted LOGO is added to the video by encryption technology, and then, LOGO recognition technology is used to distinguish whether the LOGO is missing in the video, so as to distinguish the legitimacy of the video. In addition, aiming at the shortcomings of traditional LOGO recognition technology, this paper proposes a LOGO detection and recognition technology based on convolution neural network. This method improves the traditional histogram algorithm, proposes an analysis algorithm based on non-uniform block HSV histogram, and introduces region weighting coefficients to extract key frames from the video. Then, the convolution neural network is used to extract the features of the key frames of the video. Finally, the features of the sample LOGO are matched to complete the LOGO detection and recognition. Simulation results show that the effect of feature extraction using convolution neural network is better. Compared with the traditional LOGO recognition technology, the LOGO detection and recognition technology based on convolution neural network proposed in this paper has better detection and recognition performance. It effectively detects the LOGO information in the video and realizes the recognition of whether the video is genuine or not.

A Novel 3D Video Fingerprinting Algorithm Based on Local Feature Points

Digital rights management of the 3D contents is a crucial open issue in the 3D video industry. A novel robust fingerprinting algorithm is proposed for protecting the copyright of the 3D video. Unlike the existing algorithms extracting visual features separately from the 2D videos and the depth maps, in our algorithm a novel local stereo space is constructed according to the depth information of the pixels around the extracted local feature points in the 2D videos, and the 3D videos are processed in a holistic manner. In the proposed space, the 3D-transform-feature is extracted and aggregated into a feature matrix, and then the compact 3D video fingerprints are obtained from the eigenspace of the matrix. Our comprehensive experiments are conducted on a 3D video database, and the results have demonstrated the robustness and discrimination of the proposed algorithm. Moreover, our fingerprints cost less storage spaces than the existing approaches.

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

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.

Video Copyright Protection Using Chaotic Maps and Singular Value Decomposition in Wavelet Domain

This paper attempts to offer a new method for video copyright protection using Singular Value Decomposition (SVD) and chaotic maps in the discrete wavelet domain. To provide integrity to the watermark image, the singular values of watermark images are permuted using a hyper chaotic map and an authentication key. Based on the number of scene changes in the video, the permuted watermark image is partitioned into a number of blocks. Then the frame difference method is applied for selecting the key frames in each scene. In the embedding process, each frame in the group is transformed to single level DWT. The principal component (PC) of the watermark is embedded into the LL subband of each scene in the specific key frames and the corresponding permuted watermark PCs are added to the SVs of the scene followed by inverse SVD to obtain the watermarked scene. The suitable scaling factor is selected by a new mechanism in tune with rates of changes of PSNR and NCC. The proposed algorithm is robust against video processing attacks with better integrity of watermark data with lesser processing time. The proposed method also resolves the false-positive problem by embedding the principal component of the permuted watermark.

A novel method for copyright protection of digital videos using SWEA and ZPA technique

Digital copyright protection has become an effective way to prove the ownership and protect the multimedia contents from illegal use and unauthorized users. In order to prove the ownership of a video certain security program is embedded in a video and one of the ways of ensuring the ownership of a video is embedding the watermark in a video. In this paper, a new approach to digital video copyright protection, SWEA (Split watermark embedding algorithm) with Zero Padding Algorithm (ZPA) is proposed. With the help of this algorithm, it is hard to know the original pattern of watermark because of SWEA and minimizing the perceptual degradation of watermarked video because of ZPA. Here we are using ‘db1’ wavelet domain for embedding the watermark in the LL sub-band of the original identical frame (I-frame), based on the energy of high-frequency sub-band in an adaptive manner. SCD (Scene changed detection) is used to find out the identical frame (I-frame). The proposed algorithm has undergone various attacks, such as compression, uniform noise, Gaussian noise frame repetition and frame averaging attacks. The proposed algorithm, sustain all the above attacks and offers improved performance compared with the other methods from the literature.

Learning spatial-temporal features for video copy detection by the combination of CNN and RNN

Following the rapid developments of network multimedia, video copyright protection online has become a hot topic in recent researches. However, video copy detection is still a challenging task in the domain of video analysis and computer vision, due to the large variations in scale and illumination of the copied contents. In this paper, we propose a novel deep learning based approach, in which we jointly use the Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) to solve the specific problem of detecting copied segments in videos. We first utilize a Residual Convolutional Neural Network(ResNet) to extract content features of frame-levels, and then employ a SiameseLSTM architecture for spatial-temporal fusion and sequence matching. Finally, the copied segments are detected by a graph based temporal network. We evaluate the performance of the proposed CNN-RNN based approach on a public large scale video copy dataset called VCDB, and the experiment results demonstrate the effectiveness and high robustness of our method which achieves the significant performance improvements compared to the state of the art.

A blind and robust video watermarking based on IWT and new 3D generalized chaotic sine map

In the modern world, high-bandwidth internet access has led to the theft of all types of digital data, including digital video, and their illegal distribution. The digital video watermarking technique was introduced late last century to enforce video copyright protection. In this paper, a robust blind and secure video watermarking method is presented based on integer wavelet transform and the generalized chaotic sine map. In this method, integer wavelet transform is applied to each main frame of the standard video. Basically, watermarking techniques are evaluated based on three concepts of content quality, data resilience and data capacity. Therefore, in order to guarantee the quality of the watermarked video, the watermark is inserted in low-frequency coefficients. An appropriate security level is added to increase the efficiency and functionality by chaotic map to watermark. In addition, the normalized correlation coefficient (NCC) between the main watermark and extraction watermark is used as the main criterion of resistance measurement. The results show that the proposed method is good in terms of both quality and resistance to a variety of attacks, and the NCC values obtained in most cases are 1 or very close to 1.

Fibonacci Based Key Frame Selection and Scrambling for Video Watermarking in DWT–SVD Domain

Video copyright is the technique for hiding sensitive data in digital video. This paper aims to introduce a new technique for video copyright namely Fibonacci based keyframe selection and scrambling for video watermarking in DWT---SVD. In this methodology, scene change detection technique is employed for identifying frequent changes in the scenes. In a specific frame, each scene is selected by using keyframes, which are generated by Fibonacci sequence. The initial seeds of the Fibonacci sequence are used as authentication key for generating keyframes. Based on the limitations of the scene changes the authentication keys are generated. The watermark is embedded only in specific frame of the changed scene in LH sub band. The secret image is scrambled using Fibonacci---Lucas transform. In embedding process, singular values (SVs) of the scrambled watermark block are added to the SVs of specific frames, produce the watermarked video collectively. In extraction phase, SVD is performed on the watermarked video to extract the scrambled watermark block from key frames. After that the extracted watermark blocks are collected together to yield the complete scrambled watermark. Later it is descrambled by using the secret keys to generate the original watermark. This method is blind since the host video is not required to extract the watermark. The experimental results show that the proposed methodology resists different image and video processing attacks. In addition, the proposed methodology improves the robustness and quality of both host video and watermark.

Online multi-sprites based video watermarking robust to collusion and transcoding attacks for emerging applications

The rapid growth of network transmission of numeric videos has caused an urgent need for copyright protection against pirating. Different video watermarking techniques have been proposed to resolve this problem of ownership identification. Unfortunately, these techniques have been lacking efficiency to resist collusion attack. This paper proposes a novel dynamic approach for video watermarking using SURF (Speeded Up Robust Feature) based multi-sprite to protect the copyright of digital video. The proposed approach maximizes the compromise between invisibility, capacity, robustness against most important attacks and a reduced complexity. First, multi-sprite are generated to provide collusion and compression robustness. Then, the three YUV color space components of each sprite are marked using blind spatial algorithm that improves signature invisibility and enhances robustness. Experimental results illustrate good robustness against video transcoding, collusion and against additional several other attacks such as geometric manipulation (scaling, rotation, translation, cropping) and temporal attacks (frame dropping, frame swapping and frame rate changing). Besides, the suggested technique significantly reduces the processing time thanks to the proposed multi-sprites generation method.

A Fast and Robust Key Frame Extraction Method for Video Copyright Protection

The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on.

Copyright regulations as political and economic leverage: the case of the online video industry of China

The recent popularity of video websites has transformed video copyright into one of the most critical issues in regulating the online video industry, especially in countries where video websites have started to thrive because of loose copyright regulations and low public consciousness of copyrights. This study explores copyright regulations in the emerging video website market in China, their objectives and measures, and their effects on the industrial outlook of China. Copyright has been used as political and economic leverage in regulating and shaping the video website market, thereby transforming this industry from an unrestrained Internet medium into a politically obedient and profit-driven business. The consequences of this transformation are analyzed and discussed.

Robust watermarking of mobile video resistant against barrel distortion

In head mounted display (HMD), in order to cancel pincushion distortion, the images displayed on the mobile should be pre-warped with barrel distortion. The copyright of the mobile video should be verified on both the original view and the pre-warped virtual view. A robust watermarking resistant against barrel distortion for HMDs is proposed in this paper. Watermark mask is embedded into image in consideration of imperceptibility and robustness of watermarking. In order to detect watermark from the pre-warped image with barrel distortion, an estimation method of the barrel distortion is proposed for HMDs. Then, the same warp is enforced on the embedded watermark mask with the estimated parameters of barrel distortion. The correlation between the warped watermark and the pre-warped image is computed to predicate the existence of watermark. As shown in experimental results, watermark of mobile video can be detected not only from the original views, but also from the pre-warped virtual view. It also shows that the proposed scheme is resistant against combined barrel distortion and common post-processing, such as JPEG compression.

A Video Watermarking DRM Method Based on H.264 Compressed Domain with Low Bit‐Rate Increasement

It gets more and more important for video copyright protection, it is necessary to provide efficient H.264 compression domain watermarking for video Digital rights management(DRM). A new watermarking method based on H.264 compressed domain is proposed for video DRM, in which the embedding and extracting procedure are performed using the syntactic elements of the compressed bit stream. In this way, complete decoding is unnecessary in both embedding and extracting processes. Based on the analysis of the time and space, some appropriate sub-blocks are selected for embedding watermarks, increasing watermark robustness while reducing the declination of the visual quality. In order to avoid bit-rate increasing and strengthen the security of the proposed video watermarking scheme, only a set of nonzero coefficients quantized in different parts of macroblocks is chosen for inserting watermark. The experiment results show the proposed scheme can achieve excellent robustness against some common attacks, the proposed scheme is secure and efficient for video content DRM protection.

A Study on the Co-evolutionary Appeared in the Music Video and Media Development

In this paper on the co-evolutionary theory development through music videos, broadcast media for the basis that it can present the guidelines of the singers ' Psy ' music video appeared in transition through co-evolutionary phenomena is confirmed practices for the purposes of this study. Singer psy was the Billboard Awards Top ten winners, on Now album listing, YouTube 5 month views performance based on the results refer to as billions viewers around the world have been attested, certainly well reveal the artistic inclinations and works that conform to analyze the characteristics of performance artists, the artist's self-organizing point selected reasons. As shown in the case of the evolution of the music video, the music video of singer Psy co-evolution characteristics are as follows. First, the interaction participation was seen as a form of communication such as Madang Nol-e. Second, it was the popularity of Korean culture kitschy fun and playful sentiments and emotions through healthy deviation. Third, the branded digital convergence environment and their culture evolved through various forms of music video to the expansion of the system are reproduced again showed characteristics. The Music Video Transition to the principles of Received (February 21, 2016), Review Result (March 7, 2016) Accepted (March 14, 2016), Published (April 30, 2016) (Corresponding author) Barng Keejung, Tel.+82-42-280-2916 Fax.+82-42-280-2389 HP. 010-3775-7575 34520 Dept. of Beauty & Healthcare, Daejeon Univ., 62 Daehak-Ro Dong-Gu, Dejeon, Korea E-mail: wp2848@nate.com A Study on the Co-evolutionary Appeared in the Music Video and Media Development Copyright c 2016 HSST 440 the co-evolution theory that explains Self-Organization, Red Queen Effect, Co-evolution of System Conformity relief map in four cases can be described, since present study, these characteristics of the music video were able to determine the possibility of co-evolution.