Perceptual Quality Assessment for Video Watermarking

The reliable evaluation of the performance of watermarking algorithms is difficult. An important aspect in this process is the assessment of the visibility of the watermark. In this paper, we address this issue and propose a methodology for evaluating the visual quality of watermarked video. Using a software tool that measures different types of perceptual video artifacts, we determine the most relevant impairments and design the corresponding objective metrics. We demonstrate their performance through subjective experiments on several different watermarking algorithms and video sequences

Visual attention-based image watermarking

Imperceptibility and robustness are two complementary but fundamental requirements of any watermarking algorithm. Low strength watermarking yields high imperceptibility but exhibits poor robustness. High strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host media. If distortion due to high strength watermarking can avoid visually attentive regions, such distortions are unlikely to be noticeable to any viewer. In this paper, we exploit this concept and propose a novel visual attention-based highly robust image watermarking methodology by embedding lower and higher strength watermarks in visually salient and non-salient regions, respectively. A new low complexity wavelet domain visual attention model is proposed that allows us to design new robust watermarking algorithms. The proposed new saliency model outperforms the state-of-the-art method in joint saliency detection and low computational complexity performances. In evaluating watermarking performances, the proposed blind and non-blind algorithms exhibit increased robustness to various natural image processing and filtering attacks with minimal or no effect on image quality, as verified by both subjective and objective visual quality evaluation. Up to 25% and 40% improvement against JPEG2000 compression and common filtering attacks, respectively, are reported against the existing algorithms that do not use a visual attention model

Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p

Automatic evaluation of watermarking schemes

Many watermarking schemes are now well defined, but it is still very difficult to compare them and thus find the one which fits our needs. Since both media and attacks used for evaluation are different in each article, it is almost impossible to compare the schemes.In this article, we introduce StirMark Benchmark 4, a new automatic tool to evaluate watermarking schemes. It is written in C++, according to an object oriented model, which allows us towork on images and audio files.There are many different applications for watermarking, so we use profiles to define tests to apply according to the requested parameters of the method, and its purposes. We also propose different levels of quality on usual criteria (perceptibility, robustness and capacity) to increase the legibility of the performances obtained by the schemes. We also introduce new tests (audio, key space, falsealarms, multiple watermarking).Les méthodes de tatouage sont de plus en plus nombreuses. Néanmoins, il est difficile de les comparer et de trouver celle adaptée à ses besoins dans la mesure où les tests présentés sont très souvent différents. En effet, tant les média employés que les transformations qu'ils subissent changent d'une étude à l'autre. Dans cet article, nous présentons StirMark Benchmark 4, un outil d'évaluation automatique pour les schémas de tatouage. Il est développé en C++, selon un modèle orienté objets, ce qui nous a permis de l'adapter à la fois aux images et aux fichiers audios. Les algorithmes de tatouage étant tous dissemblables, nous utilisons des profils qui définissent les tests à appliquer aux méthodes, selon les paramètres dont elles se servent et les objectifs poursuivis. Nous proposons également des niveaux d'assurance sur les critères habituels (perceptibilité, robustesse et capacité) afin de faciliter la lisibilité des performances obtenues par les schémas. Nous présentons aussi de nouveaux tests (audio, espace des-clés, fausses alarmes, marquage multiple)

Establishing the digital chain of evidence in biometric systems

Traditionally, a chain of evidence or chain of custody refers to the chronological documentation, or paper trail, showing the seizure, custody, control, transfer, analysis, and disposition of evidence, physical or electronic. Whether in the criminal justice system, military applications, or natural disasters, ensuring the accuracy and integrity of such chains is of paramount importance. Intentional or unintentional alteration, tampering, or fabrication of digital evidence can lead to undesirable effects. We find despite the consequences at stake, historically, no unique protocol or standardized procedure exists for establishing such chains. Current practices rely on traditional paper trails and handwritten signatures as the foundation of chains of evidence.;Copying, fabricating or deleting electronic data is easier than ever and establishing equivalent digital chains of evidence has become both necessary and desirable. We propose to consider a chain of digital evidence as a multi-component validation problem. It ensures the security of access control, confidentiality, integrity, and non-repudiation of origin. Our framework, includes techniques from cryptography, keystroke analysis, digital watermarking, and hardware source identification. The work offers contributions to many of the fields used in the formation of the framework. Related to biometric watermarking, we provide a means for watermarking iris images without significantly impacting biometric performance. Specific to hardware fingerprinting, we establish the ability to verify the source of an image captured by biometric sensing devices such as fingerprint sensors and iris cameras. Related to keystroke dynamics, we establish that user stimulus familiarity is a driver of classification performance. Finally, example applications of the framework are demonstrated with data collected in crime scene investigations, people screening activities at port of entries, naval maritime interdiction operations, and mass fatality incident disaster responses

Design and analysis of Discrete Cosine Transform-based watermarking algorithms for digital images. Development and evaluation of blind Discrete Cosine Transform-based watermarking algorithms for copyright protection of digital images using handwritten signatures and mobile phone numbers.

This thesis deals with the development and evaluation of blind discrete cosine transform-based watermarking algorithms for copyright protection of digital still images using handwritten signatures and mobile phone numbers. The new algorithms take into account the perceptual capacity of each low frequency coefficients inside the Discrete Cosine Transform (DCT) blocks before embedding the watermark information. They are suitable for grey-scale and colour images. Handwritten signatures are used instead of pseudo random numbers. The watermark is inserted in the green channel of the RGB colour images and the luminance channel of the YCrCb images. Mobile phone numbers are used as watermarks for images captured by mobile phone cameras. The information is embedded multiple-times and a shuffling scheme is applied to ensure that no spatial correlation exists between the original host image and the multiple watermark copies. Multiple embedding will increase the robustness of the watermark against attacks since each watermark will be individually reconstructed and verified before applying an averaging process. The averaging process has managed to reduce the amount of errors of the extracted information. The developed watermarking methods are shown to be robust against JPEG compression, removal attack, additive noise, cropping, scaling, small degrees of rotation, affine, contrast enhancements, low-pass, median filtering and Stirmark attacks. The algorithms have been examined using a library of approximately 40 colour images of size 512 512 with 24 bits per pixel and their grey-scale versions. Several evaluation techniques were used in the experiment with different watermarking strengths and different signature sizes. These include the peak signal to noise ratio, normalized correlation and structural similarity index measurements. The performance of the proposed algorithms has been compared to other algorithms and better invisibility qualities with stronger robustness have been achieved