Spread spectrum-based video watermarking algorithms for copyright protection

Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can now benefit from hardware and software which was considered state-of-the-art several years ago. The advantages offered by the digital technologies are major but the same digital technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly possible and relatively easy, in spite of various forms of protection, but due to the analogue environment, the subsequent copies had an inherent loss in quality. This was a natural way of limiting the multiple copying of a video material. With digital technology, this barrier disappears, being possible to make as many copies as desired, without any loss in quality whatsoever. Digital watermarking is one of the best available tools for fighting this threat. The aim of the present work was to develop a digital watermarking system compliant with the recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark can be inserted in either spatial domain or transform domain, this aspect was investigated and led to the conclusion that wavelet transform is one of the best solutions available. Since watermarking is not an easy task, especially considering the robustness under various attacks several techniques were employed in order to increase the capacity/robustness of the system: spread-spectrum and modulation techniques to cast the watermark, powerful error correction to protect the mark, human visual models to insert a robust mark and to ensure its invisibility. The combination of these methods led to a major improvement, but yet the system wasn't robust to several important geometrical attacks. In order to achieve this last milestone, the system uses two distinct watermarks: a spatial domain reference watermark and the main watermark embedded in the wavelet domain. By using this reference watermark and techniques specific to image registration, the system is able to determine the parameters of the attack and revert it. Once the attack was reverted, the main watermark is recovered. The final result is a high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen

Contribution to the construction of fingerprinting and watermarking schemes to protect mobile agents and multimedia content

The main characteristic of fingerprinting codes is the need of high error-correction capacity due to the fact that they are designed to avoid collusion attacks which will damage many symbols from the codewords. Moreover, the use of fingerprinting schemes depends on the watermarking system that is used to embed the codeword into the content and how it honors the marking assumption. In this sense, even though fingerprinting codes were mainly used to protect multimedia content, using them on software protection systems seems an option to be considered. This thesis, studies how to use codes which have iterative-decoding algorithms, mainly turbo-codes, to solve the fingerprinting problem. Initially, it studies the effectiveness of current approaches based on concatenating tradicioanal fingerprinting schemes with convolutional codes and turbo-codes. It is shown that these kind of constructions ends up generating a high number of false positives. Even though this thesis contains some proposals to improve these schemes, the direct use of turbo-codes without using any concatenation with a fingerprinting code as inner code has also been considered. It is shown that the performance of turbo-codes using the appropiate constituent codes is a valid alternative for environments with hundreds of users and 2 or 3 traitors. As constituent codes, we have chosen low-rate convolutional codes with maximum free distance. As for how to use fingerprinting codes with watermarking schemes, we have studied the option of using watermarking systems based on informed coding and informed embedding. It has been discovered that, due to different encodings available for the same symbol, its applicability to embed fingerprints is very limited. On this sense, some modifications to these systems have been proposed in order to properly adapt them to fingerprinting applications. Moreover the behavior and impact over a video produced as a collusion of 2 users by the YouTube’s s ervice has been s tudied. We have also studied the optimal parameters for viable tracking of users who have used YouTube and conspired to redistribute copies generated by a collusion attack. Finally, we have studied how to implement fingerprinting schemes and software watermarking to fix the problem of malicious hosts on mobile agents platforms. In this regard, four different alternatives have been proposed to protect the agent depending on whether you want only detect the attack or avoid it in real time. Two of these proposals are focused on the protection of intrusion detection systems based on mobile agents. Moreover, each of these solutions has several implications in terms of infrastructure and complexity.Els codis fingerprinting es caracteritzen per proveir una alta capacitat correctora ja que han de fer front a atacs de confabulació que malmetran una part important dels símbols de la paraula codi. D'atra banda, la utilització de codis de fingerprinting en entorns reals està subjecta a que l'esquema de watermarking que gestiona la incrustació sigui respectuosa amb la marking assumption. De la mateixa manera, tot i que el fingerprinting neix de la protecció de contingut multimèdia, utilitzar-lo en la protecció de software comença a ser una aplicació a avaluar. En aquesta tesi s'ha estudiat com aplicar codis amb des codificació iterativa, concretament turbo-codis, al problema del rastreig de traïdors en el context del fingerprinting digital. Inicialment s'ha qüestionat l'eficàcia dels enfocaments actuals en la utilització de codis convolucionals i turbo-codis que plantegen concatenacions amb esquemes habituals de fingerprinting. S'ha demostrat que aquest tipus de concatenacions portaven, de forma implícita, a una elevada probabilitat d'inculpar un usuari innocent. Tot i que s'han proposat algunes millores sobre aquests esquemes , finalment s'ha plantejat l'ús de turbocodis directament, evitant així la concatenació amb altres esquemes de fingerprinting. S'ha demostrat que, si s'utilitzen els codis constituents apropiats, el rendiment del turbo-descodificador és suficient per a ser una alternativa aplicable en entorns amb varis centenars d'usuaris i 2 o 3 confabuladors . Com a codis constituents s'ha optat pels codis convolucionals de baix ràtio amb distància lliure màxima. Pel que fa a com utilitzar els codis de fingerprinting amb esquemes de watermarking, s'ha estudiat l'opció d'utilitzar sistemes de watermarking basats en la codificació i la incrustació informada. S'ha comprovat que, degut a la múltiple codificació del mateix símbol, la seva aplicabilitat per incrustar fingerprints és molt limitada. En aquest sentit s'ha plantejat algunes modificacions d'aquests sistemes per tal d'adaptar-los correctament a aplicacions de fingerprinting. D'altra banda s'ha avaluat el comportament i l'impacte que el servei de YouTube produeix sobre un vídeo amb un fingerprint incrustat. A més , s'ha estudiat els paràmetres òptims per a fer viable el rastreig d'usuaris que han confabulat i han utilitzat YouTube per a redistribuir la copia fruït de la seva confabulació. Finalment, s'ha estudiat com aplicar els esquemes de fingerprinting i watermarking de software per solucionar el problema de l'amfitrió maliciós en agents mòbils . En aquest sentit s'han proposat quatre alternatives diferents per a protegir l'agent en funció de si és vol només detectar l'atac o evitar-lo en temps real. Dues d'aquestes propostes es centren en la protecció de sistemes de detecció d'intrusions basats en agents mòbils. Cadascuna de les solucions té diverses implicacions a nivell d'infrastructura i de complexitat.Postprint (published version

Fingerprinting with Minimum Distance Decoding

This work adopts an information theoretic framework for the design of collusion-resistant coding/decoding schemes for digital fingerprinting. More specifically, the minimum distance decision rule is used to identify 1 out of t pirates. Achievable rates, under this detection rule, are characterized in two distinct scenarios. First, we consider the averaging attack where a random coding argument is used to show that the rate 1/2 is achievable with t=2 pirates. Our study is then extended to the general case of arbitrary $t$ highlighting the underlying complexity-performance tradeoff. Overall, these results establish the significant performance gains offered by minimum distance decoding as compared to other approaches based on orthogonal codes and correlation detectors. In the second scenario, we characterize the achievable rates, with minimum distance decoding, under any collusion attack that satisfies the marking assumption. For t=2 pirates, we show that the rate $1-H(0.25)\approx 0.188$ is achievable using an ensemble of random linear codes. For $t\geq 3$, the existence of a non-resolvable collusion attack, with minimum distance decoding, for any non-zero rate is established. Inspired by our theoretical analysis, we then construct coding/decoding schemes for fingerprinting based on the celebrated Belief-Propagation framework. Using an explicit repeat-accumulate code, we obtain a vanishingly small probability of misidentification at rate 1/3 under averaging attack with t=2. For collusion attacks which satisfy the marking assumption, we use a more sophisticated accumulate repeat accumulate code to obtain a vanishingly small misidentification probability at rate 1/9 with t=2. These results represent a marked improvement over the best available designs in the literature.Comment: 26 pages, 6 figures, submitted to IEEE Transactions on Information Forensics and Securit

Contextual biometric watermarking of fingerprint images

This research presents contextual digital watermarking techniques using face and demographic text data as multiple watermarks for protecting the evidentiary integrity of fingerprint image. The proposed techniques embed the watermarks into selected regions of fingerprint image in MDCT and DWT domains. A general image watermarking algorithm is developed to investigate the application of MDCT in the elimination of blocking artifacts. The application of MDCT has improved the performance of the watermarking technique compared to DCT. Experimental results show that modifications to fingerprint image are visually imperceptible and maintain the minutiae detail. The integrity of the fingerprint image is verified through high matching score obtained from the AFIS system. There is also a high degree of correlation between the embedded and extracted watermarks. The degree of similarity is computed using pixel-based metrics and human visual system metrics. It is useful for personal identification and establishing digital chain of custody. The results also show that the proposed watermarking technique is resilient to common image modifications that occur during electronic fingerprint transmission

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented