Protecting Ownership Rights of Videos Against Digital Piracy: An Efficient Digital Watermarking Scheme

Violation of one’s intellectual ownership rights by the others is a common problem which entertainment industry frequently faces now-a-days. Sharing of information over social media platforms such as Instagram, WhatsApp and twitter without giving credit the owner causes huge financial losses to the owner and hence needs an immediate attention. Digital watermarking is a promising technique to protect owners’ right against digital piracy. Most of the state-of-the-art techniques does not provides adequate level of resilience against majority of video specific attacks and other commonly applied attacks. Therefore, this paper proposes a highly transparent and robust video watermarking solution to protect the owners rights by first convert each video frame into YCbCr color components and then select twenty five strongest speeded-up robust features (SURF) points of the normalized luminance component as points for both watermark embedding and extraction. After applying variety of geometric, simple signal processing and video specific attacks on the watermarked video meticulous analysis is performed using popular metrics which reveals that the proposed scheme possesses high correlation value which makes it superior for practical applications against these attacks. The scheme also proposes a novel three-level impairment scale for subjective analysis which gives stable results to derive correct conclusions

Image adaptive watermarking using wavelet transform

The availability of versatile multimedia processing software and the far-reaching coverage of the interconnected networks have facilitated flawless copying, manipulations and distribution of the digital multimedia (digital video, audio, text, and images). The ever-advancing storage and retrieval technologies have also smoothed the way for large-scale multimedia database applications. However, abuses of these facilities and technologies pose pressing threats to multimedia security management in general, and multimedia copyright protection and content integrity verification in particular. Although cryptography has a long history of application to information and multimedia security, the undesirable characteristic of providing no protection to the media once decrypted has limited the feasibility of its widespread use. For example, an adversary can obtain the decryption key by purchasing a legal copy of the media but then redistribute the decrypted copies of the original. In response to these challenges; digital watermarking techniques have been proposed in the last decade. Digital watermarking is the procedure whereby secret information (the watermark) is embedded into the host multimedia content, such that it is: (1) hidden, i.e., not perceptually visible; and (2) recoverable, even after the content is degraded by different attacks such as filtering, JPEG compression, noise, cropping etc. The two basic requirements for an effective watermarking scheme, imperceptibility and robustness, conflict with each other. The main focus of this thesis is to provide good tradeoff between perceptual quality of the watermarked image and its robustness against different attacks. For this purpose, we have discussed two robust digital watermarking techniques in discrete wavelet (DWT) domain. One is fusion based watermarking, and other is spread spectrum based watermarking. Both the techniques are image adaptive and employ a contrast sensitivity based human visual system (HVS) model. The HVS models give us a direct way to determine the maximum strength of watermark signal that each portion of an image can tolerate without affecting the visual quality of the image. In fusion based watermarking technique, grayscale image (logo) is used as watermark. In watermark embedding process, both the host image and watermark image are transformed into DWT domain where their coefficients are fused according to a series combination rule that take into account contrast sensitivity characteristics of the HVS. The method repeatedly merges the watermark coefficients strongly in more salient components at the various resolution levels of the host image which provides simultaneous spatial localization and frequency spread of the watermark to provide robustness against different attacks. Watermark extraction process requires original image for watermark extraction. In spread spectrum based watermarking technique, a visually recognizable binary image is used as watermark. In watermark embedding process, the host image is transformed into DWT domain. By utilizing contrast sensitivity based HVS model, watermark bits are adaptively embedded through a pseudo-noise sequence into the middle frequency sub-bands to provide robustness against different attacks. No original image is required for watermark extraction. Simulation results of various attacks are also presented to demonstrate the robustness of both the algorithms. Simulation results verify theoretical observations and demonstrate the feasibility of the digital watermarking algorithms for use in multimedia standards

Digital video watermarking techniques for secure multimedia creation and delivery.

Chan Pik-Wah.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 111-130).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Research Objective --- p.3Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- The Structure of this Thesis --- p.6Chapter 2 --- Literature Review --- p.7Chapter 2.1 --- Security in Multimedia Communications --- p.8Chapter 2.2 --- Cryptography --- p.11Chapter 2.3 --- Digital Watermarking --- p.14Chapter 2.4 --- Essential Ingredients for Video Watermarking --- p.16Chapter 2.4.1 --- Fidelity --- p.16Chapter 2.4.2 --- Robustness --- p.17Chapter 2.4.3 --- Use of Keys --- p.19Chapter 2.4.4 --- Blind Detection --- p.20Chapter 2.4.5 --- Capacity and Speed --- p.20Chapter 2.4.6 --- Statistical Imperceptibility --- p.21Chapter 2.4.7 --- Low Error Probability --- p.21Chapter 2.4.8 --- Real-time Detector Complexity --- p.21Chapter 2.5 --- Review on Video Watermarking Techniques --- p.22Chapter 2.5.1 --- Video Watermarking --- p.25Chapter 2.5.2 --- Spatial Domain Watermarks --- p.26Chapter 2.5.3 --- Frequency Domain Watermarks --- p.30Chapter 2.5.4 --- Watermarks Based on MPEG Coding Struc- tures --- p.35Chapter 2.6 --- Comparison between Different Watermarking Schemes --- p.38Chapter 3 --- Novel Watermarking Schemes --- p.42Chapter 3.1 --- A Scene-based Video Watermarking Scheme --- p.42Chapter 3.1.1 --- Watermark Preprocess --- p.44Chapter 3.1.2 --- Video Preprocess --- p.46Chapter 3.1.3 --- Watermark Embedding --- p.48Chapter 3.1.4 --- Watermark Detection --- p.50Chapter 3.2 --- Theoretical Analysis --- p.52Chapter 3.2.1 --- Performance --- p.52Chapter 3.2.2 --- Capacity --- p.56Chapter 3.3 --- A Hybrid Watermarking Scheme --- p.60Chapter 3.3.1 --- Visual-audio Hybrid Watermarking --- p.61Chapter 3.3.2 --- Hybrid Approach with Different Water- marking Schemes --- p.69Chapter 3.4 --- A Genetic Algorithm-based Video Watermarking Scheme --- p.73Chapter 3.4.1 --- Watermarking Scheme --- p.75Chapter 3.4.2 --- Problem Modelling --- p.76Chapter 3.4.3 --- Chromosome Encoding --- p.79Chapter 3.4.4 --- Genetic Operators --- p.80Chapter 4 --- Experimental Results --- p.85Chapter 4.1 --- Test on Robustness --- p.85Chapter 4.1.1 --- Experiment with Frame Dropping --- p.87Chapter 4.1.2 --- Experiment with Frame Averaging and Sta- tistical Analysis --- p.89Chapter 4.1.3 --- Experiment with Lossy Compression --- p.90Chapter 4.1.4 --- Test of Robustness with StirMark 4.0 --- p.92Chapter 4.1.5 --- Overall Comparison --- p.98Chapter 4.2 --- Test on Fidelity --- p.100Chapter 4.2.1 --- Parameter(s) Setting --- p.101Chapter 4.2.2 --- Evaluate with PSNR --- p.101Chapter 4.2.3 --- Evaluate with MAD --- p.102Chapter 4.3 --- Other Features of the Scheme --- p.105Chapter 4.4 --- Conclusion --- p.106Chapter 5 --- Conclusion --- p.108Bibliography --- p.11

Robust digital image watermarking algorithms for copyright protection

Digital watermarking has been proposed as a solution to the problem of resolving copyright ownership of multimedia data (image, audio, video). The work presented in this thesis is concerned with the design of robust digital image watermarking algorithms for copyright protection. Firstly, an overview of the watermarking system, applications of watermarks as well as the survey of current watermarking algorithms and attacks, are given. Further, the implementation of feature point detectors in the field of watermarking is introduced. A new class of scale invariant feature point detectors is investigated and it is showed that they have excellent performances required for watermarking. The robustness of the watermark on geometrical distortions is very important issue in watermarking. In order to detect the parameters of undergone affine transformation, we propose an image registration technique which is based on use of the scale invariant feature point detector. Another proposed technique for watermark synchronization is also based on use of scale invariant feature point detector. This technique does not use the original image to determine the parameters of affine transformation which include rotation and scaling. It is experimentally confirmed that this technique gives excellent results under tested geometrical distortions. In the thesis, two different watermarking algorithms are proposed in the wavelet domain. The first algorithm belongs to the class of additive watermarking algorithms which requires the presence of original image for watermark detection. Using this algorithm the influence of different error correction codes on the watermark robustness is investigated. The second algorithm does not require the original image for watermark detection. The robustness of this algorithm is tested on various filtering and compression attacks. This algorithm is successfully combined with the aforementioned synchronization technique in order to achieve the robustness on geometrical attacks. The last watermarking algorithm presented in the thesis is developed in complex wavelet domain. The complex wavelet transform is described and its advantages over the conventional discrete wavelet transform are highlighted. The robustness of the proposed algorithm was tested on different class of attacks. Finally, in the thesis the conclusion is given and the main future research directions are suggested

Information Analysis for Steganography and Steganalysis in 3D Polygonal Meshes

Information hiding, which embeds a watermark/message over a cover signal, has recently found extensive applications in, for example, copyright protection, content authentication and covert communication. It has been widely considered as an appealing technology to complement conventional cryptographic processes in the field of multimedia security by embedding information into the signal being protected. Generally, information hiding can be classified into two categories: steganography and watermarking. While steganography attempts to embed as much information as possible into a cover signal, watermarking tries to emphasize the robustness of the embedded information at the expense of embedding capacity. In contrast to information hiding, steganalysis aims at detecting whether a given medium has hidden message in it, and, if possible, recover that hidden message. It can be used to measure the security performance of information hiding techniques, meaning a steganalysis resistant steganographic/watermarking method should be imperceptible not only to Human Vision Systems (HVS), but also to intelligent analysis. As yet, 3D information hiding and steganalysis has received relatively less attention compared to image information hiding, despite the proliferation of 3D computer graphics models which are fairly promising information carriers. This thesis focuses on this relatively neglected research area and has the following primary objectives: 1) to investigate the trade-off between embedding capacity and distortion by considering the correlation between spatial and normal/curvature noise in triangle meshes; 2) to design satisfactory 3D steganographic algorithms, taking into account this trade-off; 3) to design robust 3D watermarking algorithms; 4) to propose a steganalysis framework for detecting the existence of the hidden information in 3D models and introduce a universal 3D steganalytic method under this framework. %and demonstrate the performance of the proposed steganalysis by testing it against six well-known 3D steganographic/watermarking methods. The thesis is organized as follows. Chapter 1 describes in detail the background relating to information hiding and steganalysis, as well as the research problems this thesis will be studying. Chapter 2 conducts a survey on the previous information hiding techniques for digital images, 3D models and other medium and also on image steganalysis algorithms. Motivated by the observation that the knowledge of the spatial accuracy of the mesh vertices does not easily translate into information related to the accuracy of other visually important mesh attributes such as normals, Chapters 3 and 4 investigate the impact of modifying vertex coordinates of 3D triangle models on the mesh normals. Chapter 3 presents the results of an empirical investigation, whereas Chapter 4 presents the results of a theoretical study. Based on these results, a high-capacity 3D steganographic algorithm capable of controlling embedding distortion is also presented in Chapter 4. In addition to normal information, several mesh interrogation, processing and rendering algorithms make direct or indirect use of curvature information. Motivated by this, Chapter 5 studies the relation between Discrete Gaussian Curvature (DGC) degradation and vertex coordinate modifications. Chapter 6 proposes a robust watermarking algorithm for 3D polygonal models, based on modifying the histogram of the distances from the model vertices to a point in 3D space. That point is determined by applying Principal Component Analysis (PCA) to the cover model. The use of PCA makes the watermarking method robust against common 3D operations, such as rotation, translation and vertex reordering. In addition, Chapter 6 develops a 3D specific steganalytic algorithm to detect the existence of the hidden messages embedded by one well-known watermarking method. By contrast, the focus of Chapter 7 will be on developing a 3D watermarking algorithm that is resistant to mesh editing or deformation attacks that change the global shape of the mesh. By adopting a framework which has been successfully developed for image steganalysis, Chapter 8 designs a 3D steganalysis method to detect the existence of messages hidden in 3D models with existing steganographic and watermarking algorithms. The efficiency of this steganalytic algorithm has been evaluated on five state-of-the-art 3D watermarking/steganographic methods. Moreover, being a universal steganalytic algorithm can be used as a benchmark for measuring the anti-steganalysis performance of other existing and most importantly future watermarking/steganographic algorithms. Chapter 9 concludes this thesis and also suggests some potential directions for future work

Digital Watermarking of Images towards Content Protection.

With the rapid growth of the internet and digital media techniques over the last decade, multimedia data such as images, video and audio can easily be copied, altered and distributed over the internet without any loss in quality. Therefore, protection of ownership of multimedia data has become a very significant and challenging issue. Three novel image watermarking algorithms have been designed and implemented for copyright protection. The first proposed algorithm is based on embedding multiple watermarks in the blue channel of colour images to achieve more robustness against attacks. The second proposed algorithm aims to achieve better trade-offs between imperceptibility and robustness requirements of a digital watermarking system. It embeds a watermark in adaptive manner via classification of DCT blocks with three levels: smooth, edges and texture, implemented in the DCT domain by analyzing the values of AC coefficients. The third algorithm aims to achieve robustness against geometric attacks, which can desynchronize the location of the watermark and hence cause incorrect watermark detection. It uses geometrically invariant feature points and image normalization to overcome the problem of synchronization errors caused by geometric attacks. Experimental results show that the proposed algorithms are robust and outperform related techniques found in literature

Information embedding and retrieval in 3D printed objects

Deep learning and convolutional neural networks have become the main tools of computer vision. These techniques are good at using supervised learning to learn complex representations from data. In particular, under limited settings, the image recognition model now performs better than the human baseline. However, computer vision science aims to build machines that can see. It requires the model to be able to extract more valuable information from images and videos than recognition. Generally, it is much more challenging to apply these deep learning models from recognition to other problems in computer vision. This thesis presents end-to-end deep learning architectures for a new computer vision field: watermark retrieval from 3D printed objects. As it is a new area, there is no state-of-the-art on many challenging benchmarks. Hence, we first define the problems and introduce the traditional approach, Local Binary Pattern method, to set our baseline for further study. Our neural networks seem useful but straightfor- ward, which outperform traditional approaches. What is more, these networks have good generalization. However, because our research field is new, the problems we face are not only various unpredictable parameters but also limited and low-quality training data. To address this, we make two observations: (i) we do not need to learn everything from scratch, we know a lot about the image segmentation area, and (ii) we cannot know everything from data, our models should be aware what key features they should learn. This thesis explores these ideas and even explore more. We show how to use end-to-end deep learning models to learn to retrieve watermark bumps and tackle covariates from a few training images data. Secondly, we introduce ideas from synthetic image data and domain randomization to augment training data and understand various covariates that may affect retrieve real-world 3D watermark bumps. We also show how the illumination in synthetic images data to effect and even improve retrieval accuracy for real-world recognization applications

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Digital watermarking methods for data security and authentication

Philosophiae Doctor - PhDCryptology is the study of systems that typically originate from a consideration of the ideal circumstances under which secure information exchange is to take place. It involves the study of cryptographic and other processes that might be introduced for breaking the output of such systems - cryptanalysis. This includes the introduction of formal mathematical methods for the design of a cryptosystem and for estimating its theoretical level of securit

A Feature-Based Forensic Procedure for Splicing Forgeries Detection

Nowadays, determining if an image appeared somewhere on the web or in a magazine or is authentic or not has become crucial. Image forensics methods based on features have demonstrated so far to be very effective in detecting forgeries in which a portion of an image is cloned somewhere else onto the same image. Anyway such techniques cannot be adopted to deal with splicing attack, that is, when the image portion comes from another picture that then, usually, is not available anymore for an operation of feature match. In this paper, a procedure in which these techniques could also be employed will be shown to get rid of splicing attack by resorting to the use of some repositories of images available on the Internet like Google Images or TinEye Reverse Image Search. Experimental results are presented on some real case images retrieved on the Internet to demonstrate the capacity of the proposed procedure