Simplification Resilient LDPC-Coded Sparse-QIM Watermarking for 3D-Meshes

We propose a blind watermarking scheme for 3-D meshes which combines sparse quantization index modulation (QIM) with deletion correction codes. The QIM operates on the vertices in rough concave regions of the surface thus ensuring impeccability, while the deletion correction code recovers the data hidden in the vertices which is removed by mesh optimization and/or simplification. The proposed scheme offers two orders of magnitude better performance in terms of recovered watermark bit error rate compared to the existing schemes of similar payloads and fidelity constraints.Comment: Submitted, revised and Copyright transfered to IEEE Transactions on Multimedia, October 9th 201

An Adaptive Spread Spectrum (SS) Synchronous Data Hiding Strategy for Scalable 3D Terrain Visualization

International audienceThe diversity of clients in today's network environment compels us to think about solutions that more than satisfy their needs according to their resources. For 3D terrain visualization this translates into two main requirements, namely the scalability and synchronous unification of a disparate data that requires at least two files, the texture image and its corresponding digital elevation model (DEM). In this work the scalability is achieved through the multiresolution discrete wavelet transform (DWT) of the JPEG2000 codec. For the unification of data, a simple DWT-domain spread spectrum (SS) strategy is employed in order to synchronously hide the DEM in the corresponding texture while conserving the JPEG2000 standard file format. Highest possible quality texture is renderable due to the reversible nature of the SS data hiding. As far as DEM quality is concerned, it is ensured through the adaptation of synchronization in embedding that would exclude some highest frequency subbands. To estimate the maximum tolerable error in the DEM according to a given viewpoint, a human visual system (HVS) based psycho-visual analysis is being presented. This analysis is helpful in determining the degree of adaptation in synchronization

Robust digital watermarking techniques for multimedia protection

The growing problem of the unauthorized reproduction of digital multimedia data such as movies, television broadcasts, and similar digital products has triggered worldwide efforts to identify and protect multimedia contents. Digital watermarking technology provides law enforcement officials with a forensic tool for tracing and catching pirates. Watermarking refers to the process of adding a structure called a watermark to an original data object, which includes digital images, video, audio, maps, text messages, and 3D graphics. Such a watermark can be used for several purposes including copyright protection, fingerprinting, copy protection, broadcast monitoring, data authentication, indexing, and medical safety. The proposed thesis addresses the problem of multimedia protection and consists of three parts. In the first part, we propose new image watermarking algorithms that are robust against a wide range of intentional and geometric attacks, flexible in data embedding, and computationally fast. The core idea behind our proposed watermarking schemes is to use transforms that have different properties which can effectively match various aspects of the signal's frequencies. We embed the watermark many times in all the frequencies to provide better robustness against attacks and increase the difficulty of destroying the watermark. The second part of the thesis is devoted to a joint exploitation of the geometry and topology of 3D objects and its subsequent application to 3D watermarking. The key idea consists of capturing the geometric structure of a 3D mesh in the spectral domain by computing the eigen-decomposition of the mesh Laplacian matrix. We also use the fact that the global shape features of a 3D model may be reconstructed using small low-frequency spectral coefficients. The eigen-analysis of the mesh Laplacian matrix is, however, prohibitively expensive. To lift this limitation, we first partition the 3D mesh into smaller 3D sub-meshes, and then we repeat the watermark embedding process as much as possible in the spectral coefficients of the compressed 3D sub-meshes. The visual error of the watermarked 3D model is evaluated by computing a nonlinear visual error metric between the original 3D model and the watermarked model obtained by our proposed algorithm. The third part of the thesis is devoted to video watermarking. We propose robust, hybrid scene-based MPEG video watermarking techniques based on a high-order tensor singular value decomposition of the video image sequences. The key idea behind our approaches is to use the scene change analysis to embed the watermark repeatedly in a fixed number of the intra-frames. These intra-frames are represented as 3D tensors with two dimensions in space and one dimension in time. We embed the watermark information in the singular values of these high-order tensors, which have good stability and represent the video properties. Illustration of numerical experiments with synthetic and real data are provided to demonstrate the potential and the much improved performance of the proposed algorithms in multimedia watermarking

Watermarked 3D Object Quality Assessment

This work concerns the developing of new perceptual metrics for 3D watermarking quality assessment. Any water- marking algorithm, to be effective, requires that the distortions is inevitably introduces into the watermarked media is imperceptible. This requirements is particularly severe for watermarking of 3D objects where the visual quality of the original model has to be preserved, i.e. the visual aspect of the watermarked object have to be the same of the original one. Several methods based on the knowledge of Human Visual System (HVS) have been developed to achieve this goal for still images and video watermarking. Since now, no similar techniques for watermarking of 3D objects exist. Here, we propose a novel experimental methodology for subjective evaluations of 3D objects and two perceptual metrics for quality assessment of watermarked 3D objects. Such metrics have been developed combining roughness estimation of model surface with psychophysical data collected by subjective experiments based on the proposed methodology. The performances of the proposed metrics are deeply analyzed

Robust watermarking of point-sampled geometry

We present a new scheme for digital watermarking of point-sampled geometry based on spectral analysis. By extending existing algorithms designed for polygonal data to unstructured point clouds, our method is particularly suited for scanned models, where the watermark can be directly embedded in the raw data obtained from the 3D acquisition device. To handle large data sets efficiently, we apply a fast hierarchical clustering algorithm that partitions the model into a set of patches. Each patch is mapped into the space of eigenfunctions of an approximate Laplacian operator to obtain a decomposition of the patch surface into discrete frequency bands. The watermark is then embedded into the low frequency components to minimize visual artifacts in the model geometry. During extraction, the target model is resampled at optimal resolution using an MLS projection. After extracting a watermark from this model, the corresponding bit stream is analyzed using statistical methods based on correlation. We have applied our method to a number of point-sampled models of different geometric and topological complexity. These experiments show that our watermarking scheme is robust against numerous attacks, including low-pass filtering, resampling, affine transformations, cropping, additive random noise, and combinations of the above

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

A Method for Determining the Shape Similarity of Complex Three-Dimensional Structures to Aid Decay Restoration and Digitization Error Correction

none5noThis paper introduces a new method for determining the shape similarity of complex three-dimensional (3D) mesh structures based on extracting a vector of important vertices, ordered according to a matrix of their most important geometrical and topological features. The correlation of ordered matrix vectors is combined with perceptual definition of salient regions in order to aid detection, distinguishing, measurement and restoration of real degradation and digitization errors. The case study is the digital 3D structure of the Camino Degli Angeli, in the Urbino’s Ducal Palace, acquired by the structure from motion (SfM) technique. In order to obtain an accurate, featured representation of the matching shape, the strong mesh processing computations are performed over the mesh surface while preserving real shape and geometric structure. In addition to perceptually based feature ranking, the new theoretical approach for ranking the evaluation criteria by employing neural networks (NNs) has been proposed to reduce the probability of deleting shape points, subject to optimization. Numerical analysis and simulations in combination with the developed virtual reality (VR) application serve as an assurance to restoration specialists providing visual and feature-based comparison of damaged parts with correct similar examples. The procedure also distinguishes mesh irregularities resulting from the photogrammetry process.openVasic I.; Quattrini R.; Pierdicca R.; Frontoni E.; Vasic B.Vasic, I.; Quattrini, R.; Pierdicca, R.; Frontoni, E.; Vasic, B

A robust region-adaptive digital image watermarking system

Digital image watermarking techniques have drawn the attention of researchers and practitioners as a means of protecting copyright in digital images. The technique involves a subset of information-hiding technologies, which work by embedding information into a host image without perceptually altering the appearance of the host image. Despite progress in digital image watermarking technology, the main objectives of the majority of research in this area remain improvements in the imperceptibility and robustness of the watermark to attacks. Watermark attacks are often deliberately applied to a watermarked image in order to remove or destroy any watermark signals in the host data. The purpose of the attack is. aimed at disabling the copyright protection system offered by watermarking technology. Our research in the area of watermark attacks found a number of different types, which can be classified into a number of categories including removal attacks, geometry attacks, cryptographic attacks and protocol attacks. Our research also found that both pixel domain and transform domain watermarking techniques share similar levels of sensitivity to these attacks. The experiment conducted to analyse the effects of different attacks on watermarked data provided us with the conclusion that each attack affects the high and low frequency part of the watermarked image spectrum differently. Furthermore, the findings also showed that the effects of an attack can be alleviated by using a watermark image with a similar frequency spectrum to that of the host image. The results of this experiment led us to a hypothesis that would be proven by applying a watermark embedding technique which takes into account all of the above phenomena. We call this technique 'region-adaptive watermarking'. Region-adaptive watermarking is a novel embedding technique where the watermark data is embedded in different regions of the host image. The embedding algorithms use discrete wavelet transforms and a combination of discrete wavelet transforms and singular value decomposition, respectively. This technique is derived from the earlier hypothesis that the robustness of a watermarking process can be improved by using watermark data in the frequency spectrum that are not too dissimilar to that of the host data. To facilitate this, the technique utilises dual watermarking technologies and embeds parts of the watermark images into selected regions of the host image. Our experiment shows that our technique improves the robustness of the watermark data to image processing and geometric attacks, thus validating the earlier hypothesis. In addition to improving the robustness of the watermark to attacks, we can also show a novel use for the region-adaptive watermarking technique as a means of detecting whether certain types of attack have occurred. This is a unique feature of our watermarking algorithm, which separates it from other state-of-the-art techniques. The watermark detection process uses coefficients derived from the region-adaptive watermarking algorithm in a linear classifier. The experiment conducted to validate this feature shows that, on average, 94.5% of all watermark attacks can be correctly detected and identified