Compressed domain based robust digital video watermarking scheme to protect the copyright

Ahuja, Rakesh; Ahuja, Sachin; Gupta, Deepali; Haque, Mohd Junedul

doi:10.11591/ijeecs.v21.i2.pp1160-1167

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…These consisting of various levels, stating their effect on watermark as well as video frame vulnerability to such types of noise. As shown in table (2), quality of the extracted watermark without any file jamming is 100%, and the pattern in which it is gradually reduced in case of salt, pepper, or Gaussian noise and impact on selected frames of transmitted video file.…”

Section: Resultsmentioning

confidence: 99%

Design of Quality Improvement Technique Through Ridgelet Transform on Watermarked Video

2023

ijcsm

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Nowadays, video files are stored in a digital form within the databases of servers, from where they are transmitted through internet using communication networks. Such files are subjected to different distortions throughout various phases such as acquisition, compression, type conversion, and transmission. Due to the rapid evolution of communications over internet and mobile networks, the quality of transmitted videos has become an important criterion. Many real-time applications require video processing and transmissions, such as video on demand applications, e.g., Netflix, Hulu, Vudu, video conferences, online meetings, and e-learning sessions. Therefore, video files are getting the most preferable data type nowadays, especially video games and movies [1], [3].The quality of images or video is one of the main concerns in visual data, that is being enhanced over the years. There are two main approaches in evaluating the quality of videos or images, namely subjective quality and objective quality metrics. The subjective quality metrics depend on the opinion of several experienced individuals (observers) to determine video quality; observers determine either quality video files or impairment assessments. The objective quality metrics rely on methods and procedures to determine video file quality [2], [4].Objective quality metrics are classified into three schemes: no-reference metrics (NR), reduced reference metrics (RR), and full reference metrics (FR) [5].The metrics of FR scheme requires, availability of actual video along with degraded file together to calculate quality of video. Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are the most well-known examples of full references method [6].Reduced reference metrics (RR) scheme does not require the reference video file. But only limited amount of its data will be used with the degraded version of video file to evaluate received video quality [7], [9].The no-reference metric (NR) does not contain a copy or part of original video, whereas it contains received video only. Thus, quality measurement becomes more difficult. Instead, human observers can rate video quality using eye observation [10], [11].In this paper, we propose a model using Ridgelet transformation with watermarking, evaluating embedded watermark quality to determine the received video file's quality.ABSTRACT: with the rapid spread of the use of video files cross the world before and during the pandemic, and the availability of capabilities and tools to create, store and transmit video files of high quality. The latest studies illustrate that video files are the highest data types that had been transmitted over the internet, during video transmission or broadcast from one point to the other or over the internet, videos may be damaged for several reasons, such as packet loss or jitter, in this paper, a proposed approach of Ridgelet transformation is presented to improve received video file quality, based on embedded watermark quality present in video frames. It is implemented by extracti...

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Section: Resultsmentioning

confidence: 99%

Design of Quality Improvement Technique Through Ridgelet Transform on Watermarked Video

2023

ijcsm

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“…This section presents the pseudocode [2] related to the implementation of guided filter where G(r) denotes the mean filter of guidance image with radius 'r' and I(r) denote the mean filter of filtering input image with window radius 'r'. One of the major characteristics of Guided Filter is Image/Video enhancement [28].…”

Section: Pseudo Code Of Guided Filtermentioning

confidence: 99%

“…Out of all the forms of multimedia data, videos are considered as the highly attackable where illegal distribution is at the peak and unauthorized people are making money leading to the massive loss to the entertainment industry. Thus, to safeguard the rights of the originator, there is a demand of defensible technique like digital watermarking to identify the illegitimate use and distribution of the video objects [2]. Digital watermarking is a significant technique that inserts some secret information inside the video; it could be a logo, an image or a pattern known as a watermark that can be retrieved afterwards to shield the copyright and the possession recognition against illegal sharing and manipulation of the video content.…”

Section: Introductionmentioning

confidence: 99%

Motion-Frames Based Video Watermarking Scheme for Copyright Protection Using Guided Filtering in Wavelet Domain

Purnima¹,

Ahuja²,

Gautam³

2023

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Due to the dawn of elevated bandwidth of the internet, there exists an explosion of multimedia data exchange that makes our everyday life extremely easier. With this ease, several security issues also arise named as ownership identification, copyright protection, and illegal access to digital content. This research article proposes a prevailing technique under the scope of video watermarking based on the wavelet transform domain method with the inclusion of an explicit frame filter known as a guided filter for protecting the copyrights. The guided filter is the product of a linear model and considers the resultant image to generate the ultimate filtered output. This filter is significantly accepted for its edgepreservation and detail enhancement characteristics. Thus, the inclusion of a guided filter with the watermarking technique can efficiently confiscate the video frame noise and expresses the descriptive facets of the video object resulting in an enhanced version of the watermarked video frame. Furthermore, the said filter is considered the fastest edgepreserving filter as it exhibits a fast and fairly accurate time algorithm, in spite of the other supportive attributes like the intensity and size of the kernels. Therefore, the outcome of the investigation shows that the video watermarking approach involving wavelet transform embedded with guided filter on motion-frames of the video object is proficient and effective in a range of video watermarking application areas, including copyright protection, ownership identification, video authentication, etc.

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“…Users must choose which form of watermarking to employ based on the application since there are two types of watermarking: visible and invisible [3]. Fragile watermarking is a technique for obscuring sensitive data in the host image in the spatial domain using the least significant bit (LSB) technique whereas robust watermarking is a more reliable technique that prevents unauthorized modification and ensures the authenticity and the ownership of the digital media using frequency domain to obtain the coefficient of cover image by using transform techniques such as (DCT, DWT, SVD) [4]. An effective watermark must include various properties like imperceptibility, robustness, security, and recovery.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study Between the Fragile and Robust Watermarking Techniques and Proposing New Fragile Watermarking with Embedded Value Technique

2023

IJCCCE

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Since the Internet has been more widely used and more people have access to multimedia content, copyright hacking, and piracy have risen. By the use of watermarking techniques, security, asset protection, and authentication have all been made possible. In this paper, a comparison between fragile and robust watermarking techniques has been presented to benefit them in recent studies to increase the level of security of critical media. A new technique has been suggested when adding an embedded value (129) to each pixel of the cover image and representing it as a key to thwart the attacker, increase security, rise imperceptibility, and make the system faster in detecting the tamper from unauthorized users. Using the two watermarking types in the same system reaches better results and increases the power of the system and makes it robust against any attack and revel the modification if any at the same time. PSNR has been used as a performance metric to evaluate the study. The result of the new proposed watermark is 54. It is preferable to utilize both a fragile and a robust watermark simultaneously. Index Terms— Robust Watermarking, Fragile Watermarking, Embedded Value, Imperceptibility, Security.

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Compressed domain based robust digital video watermarking scheme to protect the copyright

Cited by 3 publications

References 13 publications

Design of Quality Improvement Technique Through Ridgelet Transform on Watermarked Video

Design of Quality Improvement Technique Through Ridgelet Transform on Watermarked Video

Motion-Frames Based Video Watermarking Scheme for Copyright Protection Using Guided Filtering in Wavelet Domain

A Comparative Study Between the Fragile and Robust Watermarking Techniques and Proposing New Fragile Watermarking with Embedded Value Technique

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