Information Analysis for Steganography and Steganalysis in 3D Polygonal Meshes

Information hiding, which embeds a watermark/message over a cover signal, has recently found extensive applications in, for example, copyright protection, content authentication and covert communication. It has been widely considered as an appealing technology to complement conventional cryptographic processes in the field of multimedia security by embedding information into the signal being protected. Generally, information hiding can be classified into two categories: steganography and watermarking. While steganography attempts to embed as much information as possible into a cover signal, watermarking tries to emphasize the robustness of the embedded information at the expense of embedding capacity. In contrast to information hiding, steganalysis aims at detecting whether a given medium has hidden message in it, and, if possible, recover that hidden message. It can be used to measure the security performance of information hiding techniques, meaning a steganalysis resistant steganographic/watermarking method should be imperceptible not only to Human Vision Systems (HVS), but also to intelligent analysis. As yet, 3D information hiding and steganalysis has received relatively less attention compared to image information hiding, despite the proliferation of 3D computer graphics models which are fairly promising information carriers. This thesis focuses on this relatively neglected research area and has the following primary objectives: 1) to investigate the trade-off between embedding capacity and distortion by considering the correlation between spatial and normal/curvature noise in triangle meshes; 2) to design satisfactory 3D steganographic algorithms, taking into account this trade-off; 3) to design robust 3D watermarking algorithms; 4) to propose a steganalysis framework for detecting the existence of the hidden information in 3D models and introduce a universal 3D steganalytic method under this framework. %and demonstrate the performance of the proposed steganalysis by testing it against six well-known 3D steganographic/watermarking methods. The thesis is organized as follows. Chapter 1 describes in detail the background relating to information hiding and steganalysis, as well as the research problems this thesis will be studying. Chapter 2 conducts a survey on the previous information hiding techniques for digital images, 3D models and other medium and also on image steganalysis algorithms. Motivated by the observation that the knowledge of the spatial accuracy of the mesh vertices does not easily translate into information related to the accuracy of other visually important mesh attributes such as normals, Chapters 3 and 4 investigate the impact of modifying vertex coordinates of 3D triangle models on the mesh normals. Chapter 3 presents the results of an empirical investigation, whereas Chapter 4 presents the results of a theoretical study. Based on these results, a high-capacity 3D steganographic algorithm capable of controlling embedding distortion is also presented in Chapter 4. In addition to normal information, several mesh interrogation, processing and rendering algorithms make direct or indirect use of curvature information. Motivated by this, Chapter 5 studies the relation between Discrete Gaussian Curvature (DGC) degradation and vertex coordinate modifications. Chapter 6 proposes a robust watermarking algorithm for 3D polygonal models, based on modifying the histogram of the distances from the model vertices to a point in 3D space. That point is determined by applying Principal Component Analysis (PCA) to the cover model. The use of PCA makes the watermarking method robust against common 3D operations, such as rotation, translation and vertex reordering. In addition, Chapter 6 develops a 3D specific steganalytic algorithm to detect the existence of the hidden messages embedded by one well-known watermarking method. By contrast, the focus of Chapter 7 will be on developing a 3D watermarking algorithm that is resistant to mesh editing or deformation attacks that change the global shape of the mesh. By adopting a framework which has been successfully developed for image steganalysis, Chapter 8 designs a 3D steganalysis method to detect the existence of messages hidden in 3D models with existing steganographic and watermarking algorithms. The efficiency of this steganalytic algorithm has been evaluated on five state-of-the-art 3D watermarking/steganographic methods. Moreover, being a universal steganalytic algorithm can be used as a benchmark for measuring the anti-steganalysis performance of other existing and most importantly future watermarking/steganographic algorithms. Chapter 9 concludes this thesis and also suggests some potential directions for future work

Steganalysis of 3D objects using statistics of local feature sets

3D steganalysis aims to identify subtle invisible changes produced in graphical objects through digital watermarking or steganography. Sets of statistical representations of 3D features, extracted from both cover and stego 3D mesh objects, are used as inputs into machine learning classifiers in order to decide whether any information was hidden in the given graphical object. The features proposed in this paper include those representing the local object curvature, vertex normals, the local geometry representation in the spherical coordinate system. The effectiveness of these features is tested in various combinations with other features used for 3D steganalysis. The relevance of each feature for 3D steganalysis is assessed using the Pearson correlation coefficient. Six different 3D watermarking and steganographic methods are used for creating the stego-objects used in the evaluation study

Steganalysis of meshes based on 3D wavelet multiresolution analysis

3D steganalysis aims to find the information hidden in 3D models and graphical objects. It is assumed that the information was hidden by 3D steganography or watermarking algorithms. A new set of 3D steganalysis features, derived by using multiresolution 3D wavelet analysis, is proposed in this research study. 3D wavelets relate a given mesh representation with its lower and higher graph resolutions by means of a set of Wavelet Coefficient Vectors (WCVs). The 3D steganalysis features are derived from transformations between a given mesh and its corresponding higher and lower resolutions. They correspond to geometric measures such as ratios and angles between various geometric measures. These features are shown to significantly increase the steganalysis accuracy when detecting watermarks which have been embedded by 3D wavelet-based watermarking algorithms. The proposed features, when used in combination with a previously proposed feature set, is shown to provide the best results in detecting the hidden information embedded by other information hiding algorithms

Steganalytic Methods for 3D Objects

This PhD thesis provides new research results in the area of using 3D features for steganalysis. The research study presented in the thesis proposes new sets of 3D features, greatly extending the previously proposed features. The proposed steganlytic feature set includes features representing the vertex normal, curvature ratio, Gaussian curvature, the edge and vertex position of the 3D objects in the spherical coordinate system. Through a second contribution, this thesis presents a 3D wavelet multiresolution analysis-based steganalytic method. The proposed method extracts the 3D steganalytic features from meshes of different resolutions. The third contribution proposes a robustness and relevance-based feature selection method for solving the cover-source mismatch problem in 3D steganalysis. This method selects those 3D features that are robust to the variation of the cover source, while preserving the relevance of such features to the class label. All the proposed methods are applied for identifying stego-meshes produced by several steganographic algorithms

Robust feature-based 3D mesh segmentation and visual mask with application to QIM 3D watermarking

The last decade has seen the emergence of 3D meshes in industrial, medical and entertainment applications. Many researches, from both the academic and the industrial sectors, have become aware of their intellectual property protection arising with their increasing use. The context of this master thesis is related to the digital rights management (DRM) issues and more particularly to 3D digital watermarking which is a technical tool that by means of hiding secret information can offer copyright protection, content authentication, content tracking (fingerprinting), steganography (secret communication inside another media), content enrichment etc. Up to now, 3D watermarking non-blind schemes have reached good levels in terms of robustness against a large set of attacks which 3D models can undergo (such as noise addition, decimation, reordering, remeshing, etc.). Unfortunately, so far blind 3D watermarking schemes do not present a good resistance to de-synchronization attacks (such as cropping or resampling). This work focuses on improving the Spread Transform Dither Modulation (STDM) application on 3D watermarking, which is an extension of the Quantization Index Modulation (QIM), through both the use of the perceptual model presented, which presents good robustness against noising and smoothing attacks, and the the application of an algorithm which provides robustness noising and smoothing attacks, and the the application of an algorithm which provides robustness against reordering and cropping attacks based on robust feature detection. Similar to other watermarking techniques, imperceptibility constraint is very important for 3D objects watermarking. For this reason, this thesis also explores the perception of the distortions related to the watermark embed process as well as to the alterations produced by the attacks that a mesh can undergo

Graph spectral domain blind watermarking

This paper proposes the first ever graph spectral domain blind watermarking algorithm. We explore the recently developed graph signal processing for spread-spectrum watermarking to authenticate the data recorded on non-Cartesian grids, such as sensor data, 3D point clouds, Lidar scans and mesh data. The choice of coefficients for embedding the watermark is driven by the model for minimisation embedding distortion and the robustness model. The distortion minimisation model is proposed to reduce the watermarking distortion by establishing the relationship between the error distortion using mean square error and the selected Graph Fourier coefficients to embed the watermark. The robustness model is proposed to improve the watermarking robustness against the attacks by establishing the relationship between the watermark extraction and the effect of the attacks, namely, additive noise and nodes data deletion. The proposed models were verified by the experimental results

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