63 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
HYBRID WATERMARKING CITRA DIGITAL MENGGUNAKAN TEKNIK DWT-DCT DAN SVD
Sebagai salah satu teknik perlindungan data multimedia, watermarking telah banyak dikembangkan. Teknik watermarking dapat dilakukan pada domain transformasi, dengan menggabungkan metode Discrete Wavelet Transform (DWT) dan Discrete Cosine Transform (DCT).Watermarking pada citra digital harus memperhatikan tiga kriteria: security, robustness, dan imperceptibility. Dua kriteria terakhir merupakan hal yang paling sering bertentangan pada watermarking domain transformasi. Singular Value Decomposition (SVD) sebagai salah satu metode yang paling populer dari aplikasi aljabar linear telah banyak dimanfaatkan dalam pengolahan sinyal termasuk watermarking. Penggabungan DWT-DCT dan SVD ditujukan untuk mengatasi konflik di antara robustness dan imperceptibility. Nilai Peak Signal to Noise Ratio (PSNR) dan Normalized Cross Correlation (NC) yang diperoleh dari percobaan menyatakan bahwa skema hybrid watermarking ini menghasilkan watermark yang tahanterhadap berbagai serangan, serta kualitas yang tinggi dari citra yang disisipi watermark. Hal ini menunjukkan bahwa konflik antara robustness dan imperceptibility yang muncul pada watermarking domain transformasi dapat diatasi.Kata kunci : Watermarking, DWT, DCT, SV
Open research issues on multi-models for complex technological systems
Abstract -We are going to report here about state of the art works on multi-models for complex technological systems both from the theoretical and practical point of view. A variety of algorithmic approaches (k-mean, dss, etc.) and applicative domains (wind farms, neurological diseases, etc.) are reported to illustrate the extension of the research area
Multibiometric security in wireless communication systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and
WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition.
First is the enrolment phase by which the database of watermarked fingerprints with
memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel.
Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present oneâs fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user.
The following three steps then involve speaker recognition including the user
responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user.
In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint
image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and
sliding neighborhood) have been followed with further two steps for embedding, and
extracting the watermark into the enhanced fingerprint image utilising Discrete
Wavelet Transform (DWT).
In the speaker recognition stage, the limitations of this technique in wireless
communication have been addressed by sending voice feature (cepstral coefficients)
instead of raw sample. This scheme is to reap the advantages of reducing the
transmission time and dependency of the data on communication channel, together
with no loss of packet. Finally, the obtained results have verified the claims
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
Privacy-preserving information hiding and its applications
The phenomenal advances in cloud computing technology have raised concerns about data privacy. Aided by the modern cryptographic techniques such as homomorphic encryption, it has become possible to carry out computations in the encrypted domain and process data without compromising information privacy. In this thesis, we study various classes of privacy-preserving information hiding schemes and their real-world applications for cyber security, cloud computing, Internet of things, etc.
Data breach is recognised as one of the most dreadful cyber security threats in which private data is copied, transmitted, viewed, stolen or used by unauthorised parties. Although encryption can obfuscate private information against unauthorised viewing, it may not stop data from illegitimate exportation. Privacy-preserving Information hiding can serve as a potential solution to this issue in such a manner that a permission code is embedded into the encrypted data and can be detected when transmissions occur.
Digital watermarking is a technique that has been used for a wide range of intriguing applications such as data authentication and ownership identification. However, some of the algorithms are proprietary intellectual properties and thus the availability to the general public is rather limited. A possible solution is to outsource the task of watermarking to an authorised cloud service provider, that has legitimate right to execute the algorithms as well as high computational capacity. Privacypreserving Information hiding is well suited to this scenario since it is operated in the encrypted domain and hence prevents private data from being collected by the cloud.
Internet of things is a promising technology to healthcare industry. A common framework consists of wearable equipments for monitoring the health status of an individual, a local gateway device for aggregating the data, and a cloud server for storing and analysing the data. However, there are risks that an adversary may attempt to eavesdrop the wireless communication, attack the gateway device or even access to the cloud server. Hence, it is desirable to produce and encrypt the data simultaneously and incorporate secret sharing schemes to realise access control. Privacy-preserving secret sharing is a novel research for fulfilling this function.
In summary, this thesis presents novel schemes and algorithms, including:
⢠two privacy-preserving reversible information hiding schemes based upon symmetric cryptography using arithmetic of quadratic residues and lexicographic permutations, respectively.
⢠two privacy-preserving reversible information hiding schemes based upon asymmetric cryptography using multiplicative and additive privacy homomorphisms, respectively.
⢠four predictive models for assisting the removal of distortions inflicted by information hiding based respectively upon projection theorem, image gradient, total variation denoising, and Bayesian inference.
⢠three privacy-preserving secret sharing algorithms with different levels of generality
A statistical framework for identifying past PRDM9 binding targets in primates
Recombination is a basic biological force, which, with mutation, plays an important role in generating new combinations of alleles in each chromosome, by shuffling and exchanging genetic material between maternal and paternal chromosomes. Multiple lines of evidence suggest that the rapidly evolving zinc-finger (ZF) protein, PRDM9, is responsible for initiating much or all of recombination in human. PRDM9 shows extreme variation in both the number and sequence of its ZFs, between species and amongst individuals, across mammals. The rapid evolution of the PRDM9 ZF array may be a response to escape a self-destructive drive called biased gene conversion, which can cause preferential transmission of hotspot disrupting alleles, and leading to erosion of vital recombination sites â and hotspot signaling motifs â in the genome through time. This research attempts to uncover ancestral PRDM9 binding targets in humans and primates. By using six primate genomes, this work involves the development of statistical methods that identifies the locations where meiotic recombination could have occurred in the past. This is achieved by looking for short words that have undergone rapid losses or gains in each lineage. As a result, we found many short and different words across lineages. In conclusion, our findings imply a rapidly evolving mechanism landscape of past PRDM9 binding targets
Enhancing dysarthria speech feature representation with empirical mode decomposition and Walsh-Hadamard transform
Dysarthria speech contains the pathological characteristics of vocal tract
and vocal fold, but so far, they have not yet been included in traditional
acoustic feature sets. Moreover, the nonlinearity and non-stationarity of
speech have been ignored. In this paper, we propose a feature enhancement
algorithm for dysarthria speech called WHFEMD. It combines empirical mode
decomposition (EMD) and fast Walsh-Hadamard transform (FWHT) to enhance
features. With the proposed algorithm, the fast Fourier transform of the
dysarthria speech is first performed and then followed by EMD to get intrinsic
mode functions (IMFs). After that, FWHT is used to output new coefficients and
to extract statistical features based on IMFs, power spectral density, and
enhanced gammatone frequency cepstral coefficients. To evaluate the proposed
approach, we conducted experiments on two public pathological speech databases
including UA Speech and TORGO. The results show that our algorithm performed
better than traditional features in classification. We achieved improvements of
13.8% (UA Speech) and 3.84% (TORGO), respectively. Furthermore, the
incorporation of an imbalanced classification algorithm to address data
imbalance has resulted in a 12.18% increase in recognition accuracy. This
algorithm effectively addresses the challenges of the imbalanced dataset and
non-linearity in dysarthric speech and simultaneously provides a robust
representation of the local pathological features of the vocal folds and
tracts
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