Hazem Hiary

Audio scrambling technique based on cellular automata

Scrambling is a process that has proved to be very effective in increasing the quality of data hiding, watermarking, and encryption applications. Cellular automata are used in diverse and numerous applications because of their ability to obtain complex global behavior from simple and localized rules. In this paper we apply cellular automata in the field of audio scrambling because of the potential it holds in breaking the correlation between audio samples effectively. We also analyze the effect of using different cellular automata types on audio scrambling and we test different cellular automata rules with different Lambda values. The scrambling degree is measured and the relation between the robustness and the scrambling degree obtained is studied. Experimental results show that the proposed technique is robust to data loss attack where 1/3 of the data is lost and that the algorithm can be applied to music and speech files of different sizes.

Digital image scrambling based on elementary cellular automata

Image scrambling is the process of converting an image to an unintelligible format, mainly for security reasons. The scrambling is considered as a pre-process or a post-process of security related applications such as watermarking, information hiding, fingerprinting, and encryption. Cellular automata are parallel models of computation that prove an interesting concept where a simple configuration can lead to a complex behavior. Since there are a lot of parameters to configure, cellular automata have many types and these types differ in terms of complexity and behavior. Cellular automata were previously used in scrambling different types of multimedia, but only complex two-dimensional automata were explored. We propose a scheme where the simplest type of cellular automata is used that is the elementary type. We test the scrambling degree for different cellular automata rules that belong to classes three and four of Wolfram’s classification which correspond to complex and chaotic behavior; we also check the effect of other parameters such as the number of generations and the boundary condition. Experimental results show that our proposed scheme outperforms other schemes based on cellular automata in terms of scrambling degree.

An efficient multi-predictor reversible data hiding algorithm based on performance evaluation of different prediction schemes

With the broad development and evolution of digital data exchange, security has become an important issue in data storage and transmission since digital data can be easily manipulated and modified. Reversible data hiding algorithms are special class of steganography that are capable of recovering the original cover image upon the extraction of the secret data. This issue is of interest in medical and military imaging applications. Many algorithms in this class exploit the idea of prediction in order to increase the embedding capacity as well as the quality of the stego image. However, the performance of these algorithms depends on the type of predictor that is being used. The main goal in this paper is to survey different predictors and evaluate their performance when employed in two classical reversible data hiding algorithms. The evaluation considered plugging 22 predictors in the two algorithms to process 1438 test images. Experimental results validated the varying capabilities of different predictors and showed that the non-causal median predictor had the best performance in the two algorithms. Further more, the paper proposes a new multi-predictor reversible data hiding algorithm. Basically, the algorithm employs multiple predictors in an extended version of the modification of prediction errors (MPE) algorithm. The algorithm takes advantage of the results obtained from the performance evaluation of different predictors to select the best set of predictors. Performance evaluation proved the ability of the proposed algorithm in increasing the embedding capacity while maintaining high stego image quality.

Segmentation and localisation of whole slide images using unsupervised learning

Digital pathology has been clinically approved for over a decade to replace traditional methods of diagnosis. Many challenges appear when digitising the whole slide scan into high resolution images including memory and time management. Whole slide images require huge memory space if the tissue is not pre-localised for the scanner. The authors propose a set of clinically motivated features representing colour, intensity, texture and location to segment and localise the tissue from the whole slide image. This step saves both the scanning time and the required memory space. On average, it reduces scanning time up to 40% depending on the tissue type. The authors propose, using unsupervised learning, to segment and localise tissue by clustering. Unlike supervised methods, this method does not require the ground truth which is time consuming for domain experts. The authors proposed method achieves an average of 96% localisation accuracy on a large dataset. Moreover, the authors outperform the previously proposed supervised learning results on the same data.

Image contrast enhancement using geometric mean filter

Histogram equalization HE is one of the most popular methods for image contrast enhancement. However, the intensity of the input image plays an important role on its performance. In particular, HE fails to enhance images with a dominant color. Therefore, several techniques were proposed to tackle this problem. Some are built for brightness preservation, and others aim to maximize the preservation of structural information. In this paper, we propose an efficient HE enhancement technique that is not only addresses brightness preservation but also both edge and structural information preservation. The proposed technique investigates the geometric mean filter for smoothing the peaks in the histogram before applying the HE. To support our claims, a set of experiments were conducted. Remarkably, through qualitative and quantitative evaluations, results demonstrate that the performance of the proposed method, when compared with a set of other state-of-the-art methods including HE, CLAHE, Log-Power, BPDFHE and DWT–SVD, shows a significant improvement especially in terms of structural and edge preservation.

Watermark location via back-lighting and recto removal

We consider the problem of locating a watermark in pages of archaic documents that have been both scanned and back-lit: the problem is of interest to codicologists in identifying and tracking paper materials. Commonly, documents of interest are worn or damaged, and all information is victim to very unfavourable signal-to-noise ratios—this is especially true of ‘hidden’ data such as watermarks and chain lines. We present an approach to recto removal, followed by highlighting of such ‘hidden’ data. The result is still of very low signal quality, and we also present a statistical approach to locate watermarks from a known lexicon of fragments. Results are presented from a comprehensively scanned nineteenth century copy of the Qur’ān. The approach has lent itself to immediate exploitation in improving known watermarks, and distinguishing between twin copies.

Algebraic Model for Handling Access Control Policies

Abstract Confidentiality of information is an important aspect that developers should take into consideration when building systems. One way to achieve confidentiality is to define access control policies that give authorization rules for allowing users to access resources. In large organizations, managing policies becomes a complex task. Usually, based on the defined policies, developers would need to manipulate policies such as composing them and enforcing predefined security constraints. In this paper, we present an algebraic model for specifying access control policies. It consists of a few number of operators which gives simplicity in specifying policies. The proposed model enables us to specify policies and enforce predefined security constraints. Furthermore, the model allows us to combine policies and analyze their effect on predefined constraints. Furthermore, it enables comparing the sensitivity of objects (e.g. files) and authority of subjects (e.g. users).

Watermark location via back-lighting modelling and verso registration

We consider the location of paper watermarks in documents that present problems such as variable paper thickness, stain and other damage. Earlier work has shown success in exploiting a computational model of backlit image acquisition – here we enhance this approach by incorporating knowledge of surface verso features. Robustly removing recto features using established techniques, we present a registration approach that permits similarly robust removal of verso, leaving only features attributable to watermark, folds, chain lines and inconsistencies of paper manufacture. Experimental results illustrate the success of the approach.

Blind audio watermarking technique based on two dimensional cellular automata

In this paper we propose a new method of digital audio watermarking based on two dimensional cellular automata; the method increases the dimension of the audio and uses cellular automata in generating the key of watermark embedding. The watermarking method is blind, and does not require the original host audio or any of its features to extract the watermark; the watermark can be easily extracted using the right key. The experimental results show that the watermarks are imperceptible; and show a high similarity between the original and the watermarked audio. Cosine similarity and peak signal-to-noise ratio were used to measure the similarity between the original audio and the watermarked audio.