Scale Invariant and Rotation Invariant Image Watermarking

The scheme proposed is an improved version of the image watermarking scheme in "Scale-Invariant Image Watermarking via Optimization Algorithm for Quantizing Randomized Statistics". The previous watermarking scheme was scale invariant but not rotation invariant. In this thesis we propose to modify the method by incorporating Zernike moment transformation to make it rotationally invariant, thus making it robust against synchronization attacks.Computer Science Departmen

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

Image forgery detection using textural features and deep learning

La croissance exponentielle et les progrès de la technologie ont rendu très pratique le partage de données visuelles, d'images et de données vidéo par le biais d’une vaste prépondérance de platesformes disponibles. Avec le développement rapide des technologies Internet et multimédia, l’efficacité de la gestion et du stockage, la rapidité de transmission et de partage, l'analyse en temps réel et le traitement des ressources multimédias numériques sont progressivement devenus un élément indispensable du travail et de la vie de nombreuses personnes. Sans aucun doute, une telle croissance technologique a rendu le forgeage de données visuelles relativement facile et réaliste sans laisser de traces évidentes. L'abus de ces données falsifiées peut tromper le public et répandre la désinformation parmi les masses. Compte tenu des faits mentionnés ci-dessus, la criminalistique des images doit être utilisée pour authentifier et maintenir l'intégrité des données visuelles. Pour cela, nous proposons une technique de détection passive de falsification d'images basée sur les incohérences de texture et de bruit introduites dans une image du fait de l'opération de falsification. De plus, le réseau de détection de falsification d'images (IFD-Net) proposé utilise une architecture basée sur un réseau de neurones à convolution (CNN) pour classer les images comme falsifiées ou vierges. Les motifs résiduels de texture et de bruit sont extraits des images à l'aide du motif binaire local (LBP) et du modèle Noiseprint. Les images classées comme forgées sont ensuite utilisées pour mener des expériences afin d'analyser les difficultés de localisation des pièces forgées dans ces images à l'aide de différents modèles de segmentation d'apprentissage en profondeur. Les résultats expérimentaux montrent que l'IFD-Net fonctionne comme les autres méthodes de détection de falsification d'images sur l'ensemble de données CASIA v2.0. Les résultats discutent également des raisons des difficultés de segmentation des régions forgées dans les images du jeu de données CASIA v2.0.The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation. Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models. Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset

Automatic Segmentation and Classification of Red and White Blood cells in Thin Blood Smear Slides

In this work we develop a system for automatic detection and classification of cytological images which plays an increasing important role in medical diagnosis. A primary aim of this work is the accurate segmentation of cytological images of blood smears and subsequent feature extraction, along with studying related classification problems such as the identification and counting of peripheral blood smear particles, and classification of white blood cell into types five. Our proposed approach benefits from powerful image processing techniques to perform complete blood count (CBC) without human intervention. The general framework in this blood smear analysis research is as follows. Firstly, a digital blood smear image is de-noised using optimized Bayesian non-local means filter to design a dependable cell counting system that may be used under different image capture conditions. Then an edge preservation technique with Kuwahara filter is used to recover degraded and blurred white blood cell boundaries in blood smear images while reducing the residual negative effect of noise in images. After denoising and edge enhancement, the next step is binarization using combination of Otsu and Niblack to separate the cells and stained background. Cells separation and counting is achieved by granulometry, advanced active contours without edges, and morphological operators with watershed algorithm. Following this is the recognition of different types of white blood cells (WBCs), and also red blood cells (RBCs) segmentation. Using three main types of features: shape, intensity, and texture invariant features in combination with a variety of classifiers is next step. The following features are used in this work: intensity histogram features, invariant moments, the relative area, co-occurrence and run-length matrices, dual tree complex wavelet transform features, Haralick and Tamura features. Next, different statistical approaches involving correlation, distribution and redundancy are used to measure of the dependency between a set of features and to select feature variables on the white blood cell classification. A global sensitivity analysis with random sampling-high dimensional model representation (RS-HDMR) which can deal with independent and dependent input feature variables is used to assess dominate discriminatory power and the reliability of feature which leads to an efficient feature selection. These feature selection results are compared in experiments with branch and bound method and with sequential forward selection (SFS), respectively. This work examines support vector machine (SVM) and Convolutional Neural Networks (LeNet5) in connection with white blood cell classification. Finally, white blood cell classification system is validated in experiments conducted on cytological images of normal poor quality blood smears. These experimental results are also assessed with ground truth manually obtained from medical experts

Framework for Automatic Identification of Paper Watermarks with Chain Codes

Title from PDF of title page viewed May 21, 2018Dissertation advisor: Reza DerakhshaniVitaIncludes bibliographical references (pages 220-235)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017In this dissertation, I present a new framework for automated description, archiving, and identification of paper watermarks found in historical documents and manuscripts. The early manufacturers of paper have introduced the embedding of identifying marks and patterns as a sign of a distinct origin and perhaps as a signature of quality. Thousands of watermarks have been studied, classified, and archived. Most of the classification categories are based on image similarity and are searchable based on a set of defined contextual descriptors. The novel method presented here is for automatic classification, identification (matching) and retrieval of watermark images based on chain code descriptors (CC). The approach for generation of unique CC includes a novel image preprocessing method to provide a solution for rotation and scale invariant representation of watermarks. The unique codes are truly reversible, providing high ratio lossless compression, fast searching, and image matching. The development of a novel distance measure for CC comparison is also presented. Examples for the complete process are given using the recently acquired watermarks digitized with hyper-spectral imaging of Summa Theologica, the work of Antonino Pierozzi (1389 – 1459). The performance of the algorithm on large datasets is demonstrated using watermarks datasets from well-known library catalogue collections.Introduction -- Paper and paper watermarks -- Automatic identification of paper watermarks -- Rotation, Scale and translation invariant chain code -- Comparison of RST_Invariant chain code -- Automatic identification of watermarks with chain codes -- Watermark composite feature vector -- Summary -- Appendix A. Watermarks from the Bernstein Collection used in this study -- Appendix B. The original and transformed images of watermarks -- Appendix C. The transformed and scaled images of watermarks -- Appendix D. Example of chain cod

Contribution des filtres LPTV et des techniques d'interpolation au tatouage numérique

Les Changements d'Horloge Périodiques (PCC) et les filtres Linéaires Variant Périodiquement dans le Temps (LPTV) sont utilisés dans le domaine des télécommunications multi-utilisateurs. Dans cette thèse, nous montrons que, dans l'ensemble des techniques de tatouage par étalement de spectre, ils peuvent se substituer à la modulation par code pseudo-aléatoire. Les modules de décodage optimal, de resynchronisation, de pré-annulation des interférences et de quantification de la transformée d'étalement s'appliquent également aux PCC et aux filtres LPTV. Pour le modèle de signaux stationnaires blancs gaussiens, ces techniques présentent des performances identiques à l'étalement à Séquence Directe (DS) classique. Cependant, nous montrons que, dans le cas d'un signal corrélé localement, la luminance d'une image naturelle notamment, la périodicité des PCC et des filtres LPTV associée à un parcours d'image de type Peano-Hilbert conduit à de meilleures performances. Les filtres LPTV sont en outre un outil plus puissant qu'une simple modulation DS. Nous les utilisons pour effectuer un masquage spectral simultanément à l'étalement, ainsi qu'un rejet des interférences de l'image dans le domaine spectral. Cette dernière technique possède de très bonnes performances au décodage. Le second axe de cette thèse est l'étude des liens entre interpolation et tatouage numérique. Nous soulignons d'abord le rôle de l'interpolation dans les attaques sur la robustesse du tatouage. Nous construisons ensuite des techniques de tatouage bénéficiant des propriétés perceptuelles de l'interpolation. La première consiste en des masques perceptuels utilisant le bruit d'interpolation. Dans la seconde, un schéma de tatouage informé est construit autour de l'interpolation. Cet algorithme, qu'on peut relier aux techniques de catégorisation aléatoire, utilise des règles d'insertion et de décodage originales, incluant un masquage perceptuel intrinsèque. Outre ces bonnes propriétés perceptuelles, il présente un rejet des interférences de l'hôte et une robustesse à diverses attaques telles que les transformations valumétriques. Son niveau de sécurité est évalué à l'aide d'algorithmes d'attaque pratiques. ABSTRACT : Periodic Clock Changes (PCC) and Linear Periodically Time Varying (LPTV) filters have previously been applied to multi-user telecommunications in the Signal and Communications group of IRIT laboratory. In this thesis, we show that in each digital watermarking scheme involving spread-spectrum, they can be substituted to modulation by a pseudo-noise. The additional steps of optimal decoding, resynchronization, pre-cancellation of interference and quantization of a spread transform apply also to PCCs and LPTV filters. For white Gaussian stationary signals, these techniques offer similar performance as classical Direct Sequence (DS) spreading. However we show that, in the case of locally correlated signals such as image luminance, the periodicity of PCCs and LPTV filters associated to a Peano-Hilbert scan leads to better performance. Moreover, LPTV filters are a more powerful tool than simple DS modulation. We use LPTV filters to conduct spectrum masking simultaneous to spreading, as well as image interference cancellation in the spectral domain. The latter technique offers good decoding performance. The second axis of this thesis is the study of the links between interpolation and digital watermarking.We stress the role of interpolation in attacks on the watermark.We propose then watermarking techniques that benefit from interpolation perceptual properties. The first technique consists in constructing perceptualmasks proportional to an interpolation error. In the second technique, an informed watermarking scheme derives form interpolation. This scheme exhibits good perceptual properties, host-interference rejection and robustness to various attacks such as valumetric transforms. Its security level is assessed by ad hoc practical attack algorithms

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Semi-Fragile Image Watermarking using Zernike Moment and Fuzzy C-Means

[[abstract]]In order to improve the detection of malicious tampering images, it is necessary to decrease the fragility of hidden watermarks, even for digital images which have been incidentally distorted. In this paper, we propose a new invariant semi-fragile digital watermarking technique based on eigenvalues and eigenvectors of real symmetric matrix generated by the four pixel pairs. A signature bit for detecting the malicious tampering of an image is generated using the dominant eigenvector. And the multi-rings Zernike transform (MRZT) is proposed to achieve geometric invariance based on the fuzzy C-means and Zernike moment. The MRZT method is to the geometric distortions even when the image is under malicious attacks. Experimental results show that this algorithm can resist high quality JPEG compression, and improve the detection performance of various malicious tampering.[[sponsorship]]高雄應用科技大學 JCIS[[conferencetype]]國際[[conferencedate]]20061008~20061011[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]高雄, 臺