Graph Spectral Domain Watermarking for Unstructured Data from Sensor Networks

The modern applications like social networks and sensors networks are increasingly used in the recent years. These applications can be represented as a weighted graph using irregular structure. Unfortunately, we cannot apply the techniques of the traditional signal processing on those graphs. In this paper, graph spread spectrum watermarking is proposed for networked sensor data authentication. Firstly, the graph spectrum is computed based on the eigenvector decomposition of the graph Laplacian. Then, graph Fourier coefficients are obtained by projecting the graph signals onto the basis functions which are the eigenvectors of the graph Laplacian. Finally, the watermark bits are embedded in the graph spectral coefficients using a watermark strength parameter varied according to the eigenvector number. We have considered two scenarios: blind and non-blind watermarking. The experimental results show that the proposed methods are robust, high capacity and result in low distortion in data. The proposed algorithms are robust to many types of attacks: noise, data modification, data deletion, rounding and down-sampling

Robust digital watermarking for compressed 3D models based on polygonal representation

Multimedia has recently played an increasingly important role in various domains, including Web applications, movies, video game and medical visualization. The rapid growth of digital media data over the Internet, on the other hand, makes it easy for anyone to access, copy, edit and distribute digital contents such as electronic documents, images, sounds and videos. Motivated by this, much research work has been dedicated to develop methods for digital data copyright protection, tracing the ownership, and preventing illegal duplication or tampering. This paper introduces a methodology of robust digital watermarking based on a well-known spherical wavelet transformation, applied to 3D compressed model based on polygonal representation using a neural network. It will be demonstrated in this work that applying a watermarking algorithm on a compressed domain of a 3D object is more effective, efficient, and robust than when applied on a normal domain

Spectral methods for multimodal data analysis

Spectral methods have proven themselves as an important and versatile tool in a wide range of problems in the fields of computer graphics, machine learning, pattern recognition, and computer vision, where many important problems boil down to constructing a Laplacian operator and finding a few of its eigenvalues and eigenfunctions. Classical examples include the computation of diffusion distances on manifolds in computer graphics, Laplacian eigenmaps, and spectral clustering in machine learning. In many cases, one has to deal with multiple data spaces simultaneously. For example, clustering multimedia data in machine learning applications involves various modalities or ``views'' (e.g., text and images), and finding correspondence between shapes in computer graphics problems is an operation performed between two or more modalities. In this thesis, we develop a generalization of spectral methods to deal with multiple data spaces and apply them to problems from the domains of computer graphics, machine learning, and image processing. Our main construction is based on simultaneous diagonalization of Laplacian operators. We present an efficient numerical technique for computing joint approximate eigenvectors of two or more Laplacians in challenging noisy scenarios, which also appears to be the first general non-smooth manifold optimization method. Finally, we use the relation between joint approximate diagonalizability and approximate commutativity of operators to define a structural similarity measure for images. We use this measure to perform structure-preserving color manipulations of a given image

Streaming 3D Meshes Using Spectral Geometry Images

National Research Foundation (NRF) Singapor

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Nouvelles méthodes de synchronisation de nuages de points 3D pour l'insertion de données cachées

This thesis addresses issues relating to the protection of 3D object meshes. For instance, these objects can be created using CAD tool developed by the company STRATEGIES. In an industrial context, 3D meshes creators need to have tools in order to verify meshes integrity, or check permission for 3D printing for example.In this context we study data hiding on 3D meshes. This approach allows us to insert information in a secure and imperceptible way in a mesh. This may be an identifier, a meta-information or a third-party content, for instance, in order to transmit secretly a texture. Data hiding can address these problems by adjusting the trade-off between capacity, imperceptibility and robustness. Generally, data hiding methods consist of two stages, the synchronization and the embedding. The synchronization stage consists of finding and ordering available components for insertion. One of the main challenges is to propose an effective synchronization method that defines an order on mesh components. In our work, we propose to use mesh vertices, specifically their geometric representation in space, as basic components for synchronization and embedding. We present three new synchronisation methods based on the construction of a Hamiltonian path in a vertex cloud. Two of these methods jointly perform the synchronization stage and the embedding stage. This is possible thanks to two new high-capacity embedding methods (from 3 to 24 bits per vertex) that rely on coordinates quantization. In this work we also highlight the constraints of this kind of synchronization. We analyze the different approaches proposed with several experimental studies. Our work is assessed on various criteria including the capacity and imperceptibility of the embedding method. We also pay attention to security aspects of the proposed methods.Cette thèse aborde les problèmes liés à la protection de maillages d'objets 3D. Ces objets peuvent, par exemple, être créés à l'aide d'outil de CAD développés par la société STRATEGIES. Dans un cadre industriel, les créateurs de maillages 3D ont besoin de disposer d'outils leur permettant de vérifier l'intégrité des maillages, ou de vérifier des autorisations pour l'impression 3D par exemple. Dans ce contexte nous étudions l'insertion de données cachées dans des maillages 3D. Cette approche permet d'insérer de façon imperceptible et sécurisée de l'information dans un maillage. Il peut s'agir d'un identifiant, de méta-informations ou d'un contenu tiers, par exemple, pour transmettre de façon secrète une texture. L'insertion de données cachées permet de répondre à ces problèmes en jouant sur le compromis entre la capacité, l'imperceptibilité et la robustesse. Généralement, les méthodes d'insertion de données cachées se composent de deux phases, la synchronisation et l'insertion. La synchronisation consiste à trouver et ordonner les éléments disponibles pour l'insertion. L'un des principaux challenges est de proposer une méthode de synchronisation 3D efficace qui définit un ordre sur les composants des maillages. Dans nos travaux, nous proposons d'utiliser les sommets du maillage, plus précisément leur représentation géométrique dans l'espace comme composants de base pour la synchronisation et l'insertion. Nous présentons donc trois nouvelles méthodes de synchronisation de la géométrie des maillages basées sur la construction d'un chemin hamiltonien dans un nuage de sommets. Deux de ces méthodes permettent de manière conjointe de synchroniser les sommets et de cacher un message. Cela est possible grâce à deux nouvelles méthodes d'insertion haute capacité (de 3 à 24 bits par sommet) qui s'appuient sur la quantification des coordonnées. Dans ces travaux nous mettons également en évidence les contraintes propres à ce type de synchronisation. Nous discutons des différentes approches proposées dans plusieurs études expérimentales. Nos travaux sont évalués sur différents critères dont la capacité et l'imperceptibilité de la méthode d'insertion. Nous portons également notre attention aux aspects sécurité des méthodes

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