Digital Watermarking for Verification of Perception-based Integrity of Audio Data

In certain application fields digital audio recordings contain sensitive content. Examples are historical archival material in public archives that preserve our cultural heritage, or digital evidence in the context of law enforcement and civil proceedings. Because of the powerful capabilities of modern editing tools for multimedia such material is vulnerable to doctoring of the content and forgery of its origin with malicious intent. Also inadvertent data modification and mistaken origin can be caused by human error. Hence, the credibility and provenience in terms of an unadulterated and genuine state of such audio content and the confidence about its origin are critical factors. To address this issue, this PhD thesis proposes a mechanism for verifying the integrity and authenticity of digital sound recordings. It is designed and implemented to be insensitive to common post-processing operations of the audio data that influence the subjective acoustic perception only marginally (if at all). Examples of such operations include lossy compression that maintains a high sound quality of the audio media, or lossless format conversions. It is the objective to avoid de facto false alarms that would be expectedly observable in standard crypto-based authentication protocols in the presence of these legitimate post-processing. For achieving this, a feasible combination of the techniques of digital watermarking and audio-specific hashing is investigated. At first, a suitable secret-key dependent audio hashing algorithm is developed. It incorporates and enhances so-called audio fingerprinting technology from the state of the art in contentbased audio identification. The presented algorithm (denoted as ”rMAC” message authentication code) allows ”perception-based” verification of integrity. This means classifying integrity breaches as such not before they become audible. As another objective, this rMAC is embedded and stored silently inside the audio media by means of audio watermarking technology. This approach allows maintaining the authentication code across the above-mentioned admissible post-processing operations and making it available for integrity verification at a later date. For this, an existent secret-key ependent audio watermarking algorithm is used and enhanced in this thesis work. To some extent, the dependency of the rMAC and of the watermarking processing from a secret key also allows authenticating the origin of a protected audio. To elaborate on this security aspect, this work also estimates the brute-force efforts of an adversary attacking this combined rMAC-watermarking approach. The experimental results show that the proposed method provides a good distinction and classification performance of authentic versus doctored audio content. It also allows the temporal localization of audible data modification within a protected audio file. The experimental evaluation finally provides recommendations about technical configuration settings of the combined watermarking-hashing approach. Beyond the main topic of perception-based data integrity and data authenticity for audio, this PhD work provides new general findings in the fields of audio fingerprinting and digital watermarking. The main contributions of this PhD were published and presented mainly at conferences about multimedia security. These publications were cited by a number of other authors and hence had some impact on their works

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Recherche par le contenu adaptée à la surveillance vidéo

Les systèmes de surveillance vidéo sont omniprésents dans les lieux publics achalandés et leur présence dans les lieux privés s'accroît sans cesse. Si un aéroport ou une gare de trains peut se permettre d'employer une équipe de surveillance pour surveiller des flux vidéo en temps réel, il est improbable qu'un particulier effectue une telle dépense pour un système de surveillance à domicile. Qui plus est, l'utilisation de vidéos de surveillance pour l'analyse criminalistique requiert souvent une analyse a posteriori des événements observés. L'historique d'enregistrement correspond souvent à plusieurs jours, voire des semaines de vidéo. Si le moment où s'est produit un événement d'intérêt est inconnu, un outil de recherche vidéo est essentiel. Un tel outil a pour objectif d'identifier les segments de vidéo dont le contenu correspond à une description approximative de l'événement (ou de l'objet) recherché. Ce mémoire présente une structure de données pour l'indexation du contenu de longues vidéos de surveillance, ainsi qu'un algorithme de recherche par le contenu basé sur cette structure. À partir de la description d'un objet basée sur des attributs tels sa taille, sa couleur et la direction de son mouvement, le système identifie en temps réel les segments de vidéo contenant des objets correspondant à cette description. Nous avons démontré empiriquement que notre système fonctionne dans plusieurs cas d'utilisation tels le comptage d'objets en mouvement, la reconnaissance de trajectoires, la détection d'objets abandonnés et la détection de véhicules stationnés. Ce mémoire comporte également une section sur l'attestation de qualité d'images. La méthode présentée permet de déterminer qualitativement le type et la quantité de distortion appliquée à l'image par un système d'acquisition. Cette technique peut être utilisée pour estimer les paramètres du système d'acquisition afin de corriger les images, ou encore pour aider au développement de nouveaux systèmes d'acquisition

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Novel Methods and Algorithms for Presenting 3D Scenes

In recent years, improvements in the acquisition and creation of 3D models gave rise to an increasing availability of 3D content and to a widening of the audience such content is created for, which brought into focus the need for effective ways to visualize and interact with it. Until recently, the task of virtual inspection of a 3D object or navigation inside a 3D scene was carried out by using human machine interaction (HMI) metaphors controlled through mouse and keyboard events. However, this interaction approach may be cumbersome for the general audience. Furthermore, the inception and spread of touch-based mobile devices, such as smartphones and tablets, redefined the interaction problem entirely, since neither mouse nor keyboards are available anymore. The problem is made even worse by the fact that these devices are typically lower power if compared to desktop machines, while high-quality rendering is a computationally intensive task. In this thesis, we present a series of novel methods for the easy presentation of 3D content both when it is already available in a digitized form and when it must be acquired from the real world by image-based techniques. In the first case, we propose a method which takes as input the 3D scene of interest and an example video, and it automatically produces a video of the input scene that resembles the given video example. In other words, our algorithm allows the user to replicate an existing video, for example, a video created by a professional animator, on a different 3D scene. In the context of image-based techniques, exploiting the inherent spatial organization of photographs taken for the 3D reconstruction of a scene, we propose an intuitive interface for the smooth stereoscopic navigation of the acquired scene providing an immersive experience without the need of a complete 3D reconstruction. Finally, we propose an interactive framework for improving low-quality 3D reconstructions obtained through image-based reconstruction algorithms. Using few strokes on the input images, the user can specify high-level geometric hints to improve incomplete or noisy reconstructions which are caused by various quite common conditions often arising for objects such as buildings, streets and numerous other human-made functional elements

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Finding Objects of Interest in Images using Saliency and Superpixels

The ability to automatically find objects of interest in images is useful in the areas of compression, indexing and retrieval, re-targeting, and so on. There are two classes of such algorithms – those that find any object of interest with no prior knowledge, independent of the task, and those that find specific objects of interest known a priori. The former class of algorithms tries to detect objects in images that stand-out, i.e. are salient, by virtue of being different from the rest of the image and consequently capture our attention. The detection is generic in this case as there is no specific object we are trying to locate. The latter class of algorithms detects specific known objects of interest and often requires training using features extracted from known examples. In this thesis we address various aspects of finding objects of interest under the topics of saliency detection and object detection. We present two saliency detection algorithms that rely on the principle of center-surround contrast. These two algorithms are shown to be superior to several state-of-the-art techniques in terms of precision and recall measures with respect to a ground truth. They output full-resolution saliency maps, are simpler to implement, and are computationally more efficient than most existing algorithms. We further establish the relevance of our saliency detection algorithms by using them for the known applications of object segmentation and image re-targeting. We first present three different techniques for salient object segmentation using our saliency maps that are based on clustering, graph-cuts, and geodesic distance based labeling. We then demonstrate the use of our saliency maps for a popular technique of content-aware image resizing and compare the result with that of existing methods. Our saliency maps prove to be a much more effective replacement for conventional gradient maps for providing automatic content-awareness. Just as it is important to find regions of interest in images, it is also important to find interesting images within a large collection of images. We therefore extend the notion of saliency detection in images to image databases. We propose an algorithm for finding salient images in a database. Apart from finding such images we also present two novel techniques for creating visually appealing summaries in the form of collages and mosaics. Finally, we address the problem of finding specific known objects of interest in images. Specifically, we deal with the feature extraction step that is a pre-requisite for any technique in this domain. In this context, we first present a superpixel segmentation algorithm that outperforms previous algorithms in terms quantitative measures of under-segmentation error and boundary recall. Our superpixel segmentation algorithm also offers several other advantages over existing algorithms like compactness, uniform size, control on the number of superpixels, and computational efficiency. We prove the effectiveness of our superpixels by deploying them in existing algorithms, specifically, an object class detection technique and a graph based algorithm, and improving their performance. We also present the result of using our superpixels in a technique for detecting mitochondria in noisy medical images