Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Vers l’anti-criminalistique en images numériques via la restauration d’images

Image forensics enjoys its increasing popularity as a powerful image authentication tool, working in a blind passive way without the aid of any a priori embedded information compared to fragile image watermarking. On its opponent side, image anti-forensics attacks forensic algorithms for the future development of more trustworthy forensics. When image coding or processing is involved, we notice that image anti-forensics to some extent shares a similar goal with image restoration. Both of them aim to recover the information lost during the image degradation, yet image anti-forensics has one additional indispensable forensic undetectability requirement. In this thesis, we form a new research line for image anti-forensics, by leveraging on advanced concepts/methods from image restoration meanwhile with integrations of anti-forensic strategies/terms. Under this context, this thesis contributes on the following four aspects for JPEG compression and median filtering anti-forensics: (i) JPEG anti-forensics using Total Variation based deblocking, (ii) improved Total Variation based JPEG anti-forensics with assignment problem based perceptual DCT histogram smoothing, (iii) JPEG anti-forensics using JPEG image quality enhancement based on a sophisticated image prior model and non-parametric DCT histogram smoothing based on calibration, and (iv) median filtered image quality enhancement and anti-forensics via variational deconvolution. Experimental results demonstrate the effectiveness of the proposed anti-forensic methods with a better forensic undetectability against existing forensic detectors as well as a higher visual quality of the processed image, by comparisons with the state-of-the-art methods.La criminalistique en images numériques se développe comme un outil puissant pour l'authentification d'image, en travaillant de manière passive et aveugle sans l'aide d'informations d'authentification pré-intégrées dans l'image (contrairement au tatouage fragile d'image). En parallèle, l'anti-criminalistique se propose d'attaquer les algorithmes de criminalistique afin de maintenir une saine émulation susceptible d'aider à leur amélioration. En images numériques, l'anti-criminalistique partage quelques similitudes avec la restauration d'image : dans les deux cas, l'on souhaite approcher au mieux les informations perdues pendant un processus de dégradation d'image. Cependant, l'anti-criminalistique se doit de remplir au mieux un objectif supplémentaire, extit{i.e.} : être non détectable par la criminalistique actuelle. Dans cette thèse, nous proposons une nouvelle piste de recherche pour la criminalistique en images numériques, en tirant profit des concepts/méthodes avancés de la restauration d'image mais en intégrant des stratégies/termes spécifiquement anti-criminalistiques. Dans ce contexte, cette thèse apporte des contributions sur quatre aspects concernant, en criminalistique JPEG, (i) l'introduction du déblocage basé sur la variation totale pour contrer les méthodes de criminalistique JPEG et (ii) l'amélioration apportée par l'adjonction d'un lissage perceptuel de l'histogramme DCT, (iii) l'utilisation d'un modèle d'image sophistiqué et d'un lissage non paramétrique de l'histogramme DCT visant l'amélioration de la qualité de l'image falsifiée; et, en criminalistique du filtrage médian, (iv) l'introduction d'une méthode fondée sur la déconvolution variationnelle. Les résultats expérimentaux démontrent l'efficacité des méthodes anti-criminalistiques proposées, avec notamment une meilleure indétectabilité face aux détecteurs criminalistiques actuels ainsi qu'une meilleure qualité visuelle de l'image falsifiée par rapport aux méthodes anti-criminalistiques de l'état de l'art

Digital forensic techniques for the reverse engineering of image acquisition chains

In recent years a number of new methods have been developed to detect image forgery. Most forensic techniques use footprints left on images to predict the history of the images. The images, however, sometimes could have gone through a series of processing and modification through their lifetime. It is therefore difficult to detect image tampering as the footprints could be distorted or removed over a complex chain of operations. In this research we propose digital forensic techniques that allow us to reverse engineer and determine history of images that have gone through chains of image acquisition and reproduction. This thesis presents two different approaches to address the problem. In the first part we propose a novel theoretical framework for the reverse engineering of signal acquisition chains. Based on a simplified chain model, we describe how signals have gone in the chains at different stages using the theory of sampling signals with finite rate of innovation. Under particular conditions, our technique allows to detect whether a given signal has been reacquired through the chain. It also makes possible to predict corresponding important parameters of the chain using acquisition-reconstruction artefacts left on the signal. The second part of the thesis presents our new algorithm for image recapture detection based on edge blurriness. Two overcomplete dictionaries are trained using the K-SVD approach to learn distinctive blurring patterns from sets of single captured and recaptured images. An SVM classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images.Open Acces

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

A Distributed and Real-time Machine Learning Framework for Smart Meter Big Data

The advanced metering infrastructure allows smart meters to collect high-resolution consumption data, thereby enabling consumers and utilities to understand their energy usage at different levels, which has led to numerous smart grid applications. Smart meter data, however, poses different challenges to developing machine learning frameworks than classic theoretical frameworks due to their big data features and privacy limitations. Therefore, in this work, we aim to address the challenges of building machine learning frameworks for smart meter big data. Specifically, our work includes three parts: 1) We first analyze and compare different learning algorithms for multi-level smart meter big data. A daily activity pattern recognition model has been developed based on non-intrusive load monitoring for appliance-level smart meter data. Then, a consensus-based load profiling and forecasting system has been proposed for individual building level and higher aggregated level smart meter data analysis; 2) Following discussion of multi-level smart meter data analysis from an offline perspective, a universal online functional analysis model has been proposed for multi-level real-time smart meter big data analysis. The proposed model consists of a multi-scale load dynamic profiling unit based on functional clustering and a multi-scale online load forecasting unit based on functional deep neural networks. The two units enable online tracking of the dynamic cluster trajectories and online forecasting of daily multi-scale demand; 3) To enable smart meter data analysis in the distributed environment, FederatedNILM was proposed, which is then combined with differential privacy to provide privacy guarantees for the appliance-level distributed machine learning framework. Based on federated deep learning enhanced with two schemes, namely the utility optimization scheme and the privacy-preserving scheme, the proposed distributed and privacy-preserving machine learning framework enables electric utilities and service providers to offer smart meter services on a large scale