Implementation of Invisible Digital Watermarking Technique for Copyright Protection using DWT-SVD and DCT

The digital watermarking is a process of hiding an information in multimedia for copyright protection. Where, one data is hidden inside another data. We implement the watermarking algorithm in frequency domain by using a combination of DWT (Discrete Wavelet Transform) and SVD (Singular Value Decomposition) with DCT (Discrete Cosine Transform) algorithms. In which the performance analysis of an invisible watermarking can be measured with comparison of MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) with respect to the embedded and extracted images respectively. Here, the invisible watermarking is used to protect copyrights of multimedia contents. The invisible watermarks are the technologies which could solve the problem of copyright protection. Which is required for ownership identification as well as the hidden information can also be identified

A Study on Invisible Digital Image and Video Watermarking Techniques

Digital watermarking was introduced as a result of rapid advancement of networked multimedia systems. It had been developed to enforce copyright technologies for cover of copyright possession. This technology is first used for still images however recently they need been developed for different multimedia objects like audio, video etc. Watermarking, that belong to the information hiding field, has seen plenty of research interest. There's a lot of work begin conducted in numerous branches in this field. The image watermarking techniques might divide on the idea of domain like spatial domain or transform domain or on the basis of wavelets. The copyright protection, capacity, security, strength etc are a number of the necessary factors that are taken in account whereas the watermarking system is intended. This paper aims to produce a detailed survey of all watermarking techniques specially focuses on image watermarking types and its applications in today’s world

Robust Video Watermarking Algorithm based on DCT-SVD approach and Encryption

Sharing of digital media content over the internet is increasing everyday .Digital watermarking is a technique used to protect the intellectual property rights of multimedia content owners. In this paper, we propose a robust video watermarking scheme that utilizes Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) for embedding a watermark into video frames. The proposed method uses encryption to make the watermark more robust against malicious attacks. The encryption key is used to modify the watermark before it is embedded in the video frames. The modified watermark is then embedded in the DCT and SVD coefficients of the video frames. The experimental results show that the proposed method provides better robustness against various attacks such as compression, noise addition, and filtering, while maintaining good perceptual quality of the watermarked video. The proposed method also shows better resistance against geometric attacks such as cropping, rotation, and scaling. Overall, the proposed method provides an effective solution for protecting the intellectual property rights of multimedia content owners in video distribution and transmission scenarios

COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT

تستخدم الإشارات الصوتية التي يولدها جسم الإنسان بشكل روتيني من قبل التخصصين في البحوث والتطبيقات الصحية  للمساعدة في تشخيص بعض  الامراض أو تقييم تقدم المرض. وبالنظر إلى التقنيات الجديدة ، من الممكن في الوقت الحاضر جمع الأصوات التي يولدها الإنسان ، مثل السعال. ويمكن بعد ذلك اعتماد تقنيات التعلم الآلي المستندة إلى الصوت من أجل التحليل التلقائي للبيانات التي تم جمعها مما يوفر معلومات قيمة غنية من إشارة السعال واستخراج الميزات الفعالة من فترة زمنية محدودة الطول تتغير كدالة للوقت. في هذا البحث يتم اقتراح وتقديم خوارزمية  للكشف عن COVID-19 وتشخيصه من خلال معالجة السعال الذي يتم جمعه من المرضى الذين يعانون من الأعراض الأكثر شيوعًا لهذا الوباء. تعتمد الطريقة المقترحة على اعتماد مزيج من تحليل القيمة المفردة (SVD) وتحويل المويجات المنفصل (DWT).  وقد أدى الجمع بين هاتين التقنيتين لمعالجة الإشارات إلى اتباع نهج جيد للتعرف على السعال ، حيث يولد ويستخدم الحد الأدنى من الميزات الفعالة. وفي هذه الخوارزمية المقترحة يتم تطبيق الترددات المتوسطة (mean and median)، والمعروفة بأنها أكثر الميزات المفيدة في مجال التردد ، لإنشاء مقياس إحصائي فعال لمقارنة النتائج. بالإضافة إلى الحصول على معدل كشف وتمييز عاليين ، تتميز الخوارزمية المقترحة بكفاءتها حيث يتم تحقيق تخفيض 200 مرة، من حيث عدد العمليات. على الرغم من حقيقة أن أعراض الأشخاص المصابين وغير المصابين في الدراسة بها الكثير من أوجه التشابه ، فإن نتائج التشخيص التي تم الحصول عليها من تطبيق نهجنا تُظهر معدل تشخيص مرتفعًا، والذي تم إثباته من خلال مطابقتها مع اختبارات PCR ذات الصلة. نعتقد أنه يمكن تحقيق أداء أفضل من خلال توسيع مجموعة البيانات ، مع تضمين الأشخاص الأصحاء.Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopting a combination of Singular Value Decomposition (SVD), and Discrete Wavelet Transform (DWT). The combination of these two signal processing techniques is gaining lots of interest in the field of speaker and speech recognition. As a cough recognition approach, we found it well-performing, as it generates and utilizes an efficient minimum number of features. Mean and median frequencies, which are known to be the most useful features in the frequency domain, are applied to generate an effective statistical measure to compare the results. The hybrid structure of DWT and SVD, adopted in this approach adds to its efficiency, where a 200 times reduction, in terms of the number of operations, is achieved. Despite the fact that symptoms of the infected and non-infected people used in the study are having lots of similarities, diagnosis results obtained from the application of the proposed approach show high diagnosis rate, which is proved through the matching with relevant PCR tests.  The proposed approach is open for more improvements with its performance further assured by enlarging the dataset, while including healthy people

Audio, Text, Image, and Video Digital Watermarking Techniques for Security of Media Digital

The proliferation of multimedia content as digital media assets, encompassing audio, text, images, and video, has led to increased risks of unauthorized usage and copyright infringement. Online piracy serves as a prominent example of such misuse. To address these challenges, watermarking techniques have been developed to protect the copyright of digital media while maintaining the integrity of the underlying content. Key characteristics evaluated in watermarking methods include capability, privacy, toughness, and invisibility, with robustness playing a crucial role. This paper presents a comparative analysis of digital watermarking methods, highlighting the superior security and effective watermark image recovery offered by singular value decomposition. The research community has shown significant interest in watermarking, resulting in the development of various methods in both the spatial and transform domains. Transform domain approaches such as Discrete Cosine Transform, Discrete Wavelet Transform, and Singular Value Decomposition, along with their interconnections, have been explored to enhance the effectiveness of digital watermarking methods

Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM

The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work