26 research outputs found
Symmetry-Adapted Machine Learning for Information Security
Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis
Fast fallback watermark detection using perceptual hashes
Forensic watermarking is often used to enable the tracing of digital pirates that leak copyright-protected videos. However, existing watermarking methods have a limited robustness and may be vulnerable to targeted attacks. Our previous work proposed a fallback detection method that uses secondary watermarks rather than the primary watermarks embedded by existing methods. However, the previously proposed fallback method is slow and requires access to all watermarked videos. This paper proposes to make the fallback watermark detection method faster using perceptual hashes instead of uncompressed secondary watermark signals. These perceptual hashes can be calculated prior to detection, such that the actual detection process is sped up with a factor of approximately 26,000 to 92,000. In this way, the proposed method tackles the main criticism about practical usability of the slow fallback method. The fast detection comes at the cost of a modest decrease in robustness, although the fast fallback detection method can still outperform the existing primary watermark method. In conclusion, the proposed method enables fast and more robust detection of watermarks that were embedded by existing watermarking methods
Digital watermark technology in security applications
With the rising emphasis on security and the number of fraud related crimes
around the world, authorities are looking for new technologies to tighten
security of identity. Among many modern electronic technologies, digital
watermarking has unique advantages to enhance the document authenticity.
At the current status of the development, digital watermarking technologies
are not as matured as other competing technologies to support identity authentication
systems. This work presents improvements in performance of
two classes of digital watermarking techniques and investigates the issue of
watermark synchronisation.
Optimal performance can be obtained if the spreading sequences are designed
to be orthogonal to the cover vector. In this thesis, two classes of
orthogonalisation methods that generate binary sequences quasi-orthogonal
to the cover vector are presented. One method, namely "Sorting and Cancelling"
generates sequences that have a high level of orthogonality to the
cover vector. The Hadamard Matrix based orthogonalisation method, namely
"Hadamard Matrix Search" is able to realise overlapped embedding, thus the
watermarking capacity and image fidelity can be improved compared to using
short watermark sequences. The results are compared with traditional
pseudo-randomly generated binary sequences. The advantages of both classes
of orthogonalisation inethods are significant.
Another watermarking method that is introduced in the thesis is based
on writing-on-dirty-paper theory. The method is presented with biorthogonal
codes that have the best robustness. The advantage and trade-offs of
using biorthogonal codes with this watermark coding methods are analysed
comprehensively. The comparisons between orthogonal and non-orthogonal
codes that are used in this watermarking method are also made. It is found
that fidelity and robustness are contradictory and it is not possible to optimise
them simultaneously.
Comparisons are also made between all proposed methods. The comparisons
are focused on three major performance criteria, fidelity, capacity and
robustness. aom two different viewpoints, conclusions are not the same. For
fidelity-centric viewpoint, the dirty-paper coding methods using biorthogonal
codes has very strong advantage to preserve image fidelity and the advantage
of capacity performance is also significant. However, from the power
ratio point of view, the orthogonalisation methods demonstrate significant
advantage on capacity and robustness. The conclusions are contradictory
but together, they summarise the performance generated by different design
considerations.
The synchronisation of watermark is firstly provided by high contrast
frames around the watermarked image. The edge detection filters are used
to detect the high contrast borders of the captured image. By scanning
the pixels from the border to the centre, the locations of detected edges
are stored. The optimal linear regression algorithm is used to estimate the
watermarked image frames. Estimation of the regression function provides
rotation angle as the slope of the rotated frames. The scaling is corrected by
re-sampling the upright image to the original size. A theoretically studied
method that is able to synchronise captured image to sub-pixel level accuracy
is also presented. By using invariant transforms and the "symmetric
phase only matched filter" the captured image can be corrected accurately
to original geometric size. The method uses repeating watermarks to form an
array in the spatial domain of the watermarked image and the the array that
the locations of its elements can reveal information of rotation, translation
and scaling with two filtering processes
Applications of MATLAB in Science and Engineering
The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest
Robust Logo Watermarking
Digital image watermarking is used to protect the copyright of digital images. In this thesis, a novel blind logo image watermarking technique for RGB images is proposed. The proposed technique exploits the error correction capabilities of the Human Visual System (HVS). It embeds two different watermarks in the wavelet/multiwavelet domains. The two watermarks are embedded in different sub-bands, are orthogonal, and serve different purposes. One is a high capacity multi-bit watermark used to embed the logo, and the other is a 1-bit watermark which is used for the detection and reversal of geometrical attacks. The two watermarks are both embedded using a spread spectrum approach, based on a pseudo-random noise (PN) sequence and a unique secret key. Robustness against geometric attacks such as Rotation, Scaling, and Translation (RST) is achieved by embedding the 1-bit watermark in the Wavelet Transform Modulus Maxima (WTMM) coefficients of the wavelet transform. Unlike normal wavelet coefficients, WTMM coefficients are shift invariant, and this important property is used to facilitate the detection and reversal of RST attacks. The experimental results show that the proposed watermarking technique has better distortion parameter detection capabilities, and compares favourably against existing techniques in terms of robustness against geometrical attacks such as rotation, scaling, and translation
Recommended from our members
Fast embedding for image classification & retrieval and its application to the hostel industry
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking
an unseen image input and extracting its features representing the input image. Then,
for the classification task, this mathematically measured input is categorized according
to established criteria in the server and consequently shows the output as a result. On
the other hand, for the retrieval task, the extracted features of an unseen query image
are sent to the server to search for the most visually similar images to a given image
and retrieve these images as a result. Despite image features could be represented
by classical features, artificial intelligence-based features, Convolutional Neural
Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless,
the high dimensional CNN features have been a challenge in particular for applications
on mobile or Internet of Things devices. Therefore, in this thesis, several fast
embeddings are explored and proposed to overcome the constraints of low memory,
bandwidth, and power. Furthermore, the first hostel image database is created with
three datasets, hostel image dataset containing 13,908 interior and exterior images of
hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing
972 images and 2,380 images, respectively, of 20 London hostel buildings. The results
demonstrate that the proposed fast embeddings such as the application of GHM-Rand
operator, GHM-Fix operator, and binary feature vectors are able to outperform or give
competitive results to those state-of-the-art methods with a lot less computational
resource. Additionally, the findings from a ten-year literature review of CBIR study in
the tourism industry could picturize the relevant research activities in the past decade
which are not only beneficial to the hostel industry or tourism sector but also to the
computer science and engineering research communities for the potential real-life
applications of the existing and developing technologies in the field
Recent Advances in Signal Processing
The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity