The framework of P systems applied to solve optimal watermarking problem

Membrane computing (known as P systems) is a novel class of distributed parallel computing models inspired by the structure and functioning of living cells and organs, and its application to the real-world problems has become a hot topic in recent years. This paper discusses an interesting open problem in digital watermarking domain, optimal watermarking problem, and proposes a new optimal image watermarking method under the framework of P systems. A special membrane structure is designed and its cells as parallel computing units are used to find the optimal watermarking parameters for image blocks. Some cells use the position-velocity model to evolve watermarking parameters of image blocks, while another cell evaluates the objects in the system. In addition to the evolution rules, communication rules are used to exchange and share information between the cells. Simulation experiments on large image set compare the proposed framework with other existing watermarking methods and demonstrate its superiority.National Natural Science Foundation of China No 61170030Chunhui Project Foundation of the Education Department of China No. Z2012025Chunhui Project Foundation of the Education Department of China No. Z2012031Sichuan Key Technology Research and Development Program No. 2013GZX015

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

Over 725 steganography tools are available over the Internet, each providing a method for covert transmission of secret messages. This research presents four steganalysis advancements that result in an algorithm that identifies the steganalysis tool used to embed a secret message in a JPEG image file. The algorithm includes feature generation, feature preprocessing, multi-class classification and classifier fusion. The first contribution is a new feature generation method which is based on the decomposition of discrete cosine transform (DCT) coefficients used in the JPEG image encoder. The generated features are better suited to identifying discrepancies in each area of the decomposed DCT coefficients. Second, the classification accuracy is further improved with the development of a feature ranking technique in the preprocessing stage for the kernel Fisher s discriminant (KFD) and support vector machines (SVM) classifiers in the kernel space during the training process. Third, for the KFD and SVM two-class classifiers a classification tree is designed from the kernel space to provide a multi-class classification solution for both methods. Fourth, by analyzing a set of classifiers, signature detectors, and multi-class classification methods a classifier fusion system is developed to increase the detection accuracy of identifying the embedding method used in generating the steganography images. Based on classifying stego images created from research and commercial JPEG steganography techniques, F5, JP Hide, JSteg, Model-based, Model-based Version 1.2, OutGuess, Steganos, StegHide and UTSA embedding methods, the performance of the system shows a statistically significant increase in classification accuracy of 5%. In addition, this system provides a solution for identifying steganographic fingerprints as well as the ability to include future multi-class classification tools

Facial image encryption for secure face recognition system

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks

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

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

Algorithmic Analysis of Complex Audio Scenes

In this thesis, we examine the problem of algorithmic analysis of complex audio scenes with a special emphasis on natural audio scenes. One of the driving goals behind this work is to develop tools for monitoring the presence of animals in areas of interest based on their vocalisations. This task, which often occurs in the evaluation of nature conservation measures, leads to a number of subproblems in audio scene analysis. In order to develop and evaluate pattern recognition algorithms for animal sounds, a representative collection of such sounds is necessary. Building such a collection is beyond the scope of a single researcher and we therefore use data from the Animal Sound Archive of the Humboldt University of Berlin. Although a large portion of well annotated recordings from this archive has been available in digital form, little infrastructure for searching and sharing this data has been available. We describe a distributed infrastructure for searching, sharing and annotating animal sound collections collaboratively, which we have developed in this context. Although searching animal sound databases by metadata gives good results for many applications, annotating all occurences of a specific sound is beyond the scope of human annotators. Moreover, finding similar vocalisations to that of an example is not feasible by using only metadata. We therefore propose an algorithm for content-based similarity search in animal sound databases. Based on principles of image processing, we develop suitable features for the description of animal sounds. We enhance a concept for content-based multimedia retrieval by a ranking scheme which makes it an efficient tool for similarity search. One of the main sources of complexity in natural audio scenes, and the most difficult problem for pattern recognition, is the large number of sound sources which are active at the same time. We therefore examine methods for source separation based on microphone arrays. In particular, we propose an algorithm for the extraction of simpler components from complex audio scenes based on a sound complexity measure. Finally, we introduce pattern recognition algorithms for the vocalisations of a number of bird species. Some of these species are interesting for reasons of nature conservation, while one of the species serves as a prototype for song birds with strongly structured songs.Algorithmische Analyse Komplexer Audioszenen In dieser Arbeit untersuchen wir das Problem der Analyse komplexer Audioszenen mit besonderem Augenmerk auf natürliche Audioszenen. Eine der treibenden Zielsetzungen hinter dieser Arbeit ist es Werkzeuge zu entwickeln, die es erlauben ein auf Lautäußerungen basierendes Monitoring von Tierarten in Zielregionen durchzuführen. Diese Aufgabenstellung, die häufig in der Evaluation von Naturschutzmaßnahmen auftritt, führt zu einer Anzahl von Unterproblemen innerhalb der Audioszenen-Analyse. Eine wichtige Voraussetzung um Mustererkennungs-Algorithmen für Tierstimmen entwickeln zu können, ist die Verfügbarkeit großer Sammlungen von Aufnahmen von Tierstimmen. Eine solche Sammlung aufzubauen liegt jenseits der Möglichkeiten eines einzelnen Forschers und wir verwenden daher Daten des Tierstimmenarchivs der Humboldt Universität Berlin. Obwohl eine große Anzahl gut annotierter Aufnahmen in diesem Archiv in digitaler Form vorlagen, gab es nur wenig unterstützende Infrastruktur um diese Daten durchsuchen und verteilen zu können. Wir beschreiben eine verteilte Infrastruktur, mit deren Hilfe es möglich ist Tierstimmen-Sammlungen zu durchsuchen, sowie gemeinsam zu verwenden und zu annotieren, die wir in diesem Kontext entwickelt haben. Obwohl das Durchsuchen von Tierstimmen-Datenbank anhand von Metadaten für viele Anwendungen gute Ergebnisse liefert, liegt es jenseits der Möglichkeiten menschlicher Annotatoren alle Vorkommen eines bestimmten Geräuschs zu annotieren. Darüber hinaus ist es nicht möglich einem Beispiel ähnlich klingende Geräusche nur anhand von Metadaten zu finden. Deshalb schlagen wir einen Algorithmus zur inhaltsbasierten Ähnlichkeitssuche in Tierstimmen-Datenbanken vor. Ausgehend von Methoden der Bildverarbeitung entwickeln wir geeignete Merkmale für die Beschreibung von Tierstimmen. Wir erweitern ein Konzept zur inhaltsbasierten Multimedia-Suche um ein Ranking-Schema, dass dieses zu einem effizienten Werkzeug für die Ähnlichkeitssuche macht. Eine der grundlegenden Quellen von Komplexität in natürlichen Audioszenen, und das schwierigste Problem für die Mustererkennung, stellt die hohe Anzahl gleichzeitig aktiver Geräuschquellen dar. Deshalb untersuchen wir Methoden zur Quellentrennung, die auf Mikrofon-Arrays basieren. Insbesondere schlagen wir einen Algorithmus zur Extraktion einfacherer Komponenten aus komplexen Audioszenen vor, der auf einem Maß für die Komplexität von Audioaufnahmen beruht. Schließlich führen wir Mustererkennungs-Algorithmen für die Lautäußerungen einer Reihe von Vogelarten ein. Einige dieser Arten sind aus Gründen des Naturschutzes interessant, während eine Art als Prototyp für Singvögel mit stark strukturierten Gesängen dient

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