Rasber Dh. Rashid

Salahaddin University-Erbil

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Co-Authors

Sabah Jassim

University of Buckingham

Taban Majeed

Salahaddin University-Erbil

Aras Asaad

University of Buckingham

Dashti Ali

University of Basrah

Harin Sellahewa

University of Buckingham

Zanear Sh. Ahmed

Sulaimani Polytechnic University

Shadman Q. Salih

Sulaimani Polytechnic University

Nigar M. Shafiq Surameery

Kurdistan Regional Government

Hawre Kh. Abdulla

Sulaimani Polytechnic University

Hisham Al-Assam

University of Buckingham

Cooperative Institutions

Koya University

University of Buckingham

Sulaimani Polytechnic University

Salahaddin University-Erbil

Oxford Drug Design (United Kingdom)

University of Basrah

Near East University

Mersin Üniversitesi

European University of Lefke

Middle East Technical University

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Combining steganography and biometric cryptosystems for secure mutual authentication and key exchange

Hisham Al-Assam Rasber Dh. Rashid Sabah Jassim

Human development urgently needs appropriate philosophical conception for society to cultivate global talents in information age. Positive critique based on Chinese and western culture is used to explore the influence on the level of human development. This project studies the framework of positivity, critique, and judgment and constructed the theoretical model of positive critique influence on human development. Drawing on patent data of enterprises with international business in Hebei province, this paper verified theoretical model and research hypotheses by structural equation model, and analyzed the path of the impact positivity, critique and judgment on human development. The results show that there is a positive correlation between positivity, critique, and judgment and the positive critique affects the level of human development.

10.1109/icist.2013.6747573

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Citations (11)

Biometric feature embedding using robust steganography technique

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2013)

Rasber Dh. Rashid Harin Sellahewa Sabah Jassim

This paper is concerned with robust steganographic techniques to hide and communicate biometric data in mobile media objects like images, over open networks. More specifically, the aim is to embed binarised features extracted using discrete wavelet transforms and local binary patterns of face images as a secret message in an image. The need for such techniques can arise in law enforcement, forensics, counter terrorism, internet/mobile banking and border control. What differentiates this problem from normal information hiding techniques is the added requirement that there should be minimal effect on face recognition accuracy. We propose an LSB-Witness embedding technique in which the secret message is already present in the LSB plane but instead of changing the cover image LSB values, the second LSB plane will be changed to stand as a witness/informer to the receiver during message recovery. Although this approach may affect the stego quality, it is eliminating the weakness of traditional LSB schemes that is exploited by steganalysis techniques for LSB, such as PoV and RS steganalysis, to detect the existence of secrete message. Experimental results show that the proposed method is robust against PoV and RS attacks compared to other variants of LSB. We also discussed variants of this approach and determine capacity requirements for embedding face biometric feature vectors while maintain accuracy of face recognition.

Steganalysis

Feature (linguistics)

10.1117/12.2018910

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Citations (8)

CNN-based Transfer Learning for Covid-19 Diagnosis

Zanear Sh. Ahmed Nigar M. Shafiq Surameery Rasber Dh. Rashid Shadman Q. Salih Hawre Kh. Abdulla

CNN-based transfer learning method plays a significant role in the detection of various objects such as cars, dogs, motorcycles, face and human detection in nighttime images by using visible light camera sensors. This method mainly depends on the images captured by cameras in order to detect the mentioned objects in a variety of environments based on convolutional neural networks (CNNs). In this study, we utilized the same method to detect coronavirus phenomena by using chest X-ray images that have been collected from three different open-source datasets with the aim of rapid detection of the infected patients and speed up the diagnostic process. We used one of the deep learning architectures in a Transfer Learning mode and modified its final layers to adapt to the number of classes in our investigation. The deep learning architecture that we used for the purpose of COVID-19 detection from X-ray images is a CNN designed to detect human in nighttime. We also modified the CNN architecture in three different scenarios named (Model 1, Model 2 and Model 3) in order to improve the classification results. Compared to model one and two, the result improved in model three and the number of misclassified cases reduced particularly in detecting Abnormal and COVID-19 cases. Although our CNN-based method shows high performance in COVID-19 detection, CNN decisions should not to be taken into consideration until clinical tests confirms symptoms of the infected patients.

Transfer of learning

10.1109/icit52682.2021.9491126

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Citations (3)

Combining steganography and biometric cryptosystems for secure mutual authentication and key exchange

Hisham Al-Assam Rasber Dh. Rashid Sabah Jassim

Although biometric authentication is perceived to be more reliable than traditional authentication schemes, it becomes vulnerable to several attacks when it comes to remote authentication over open networks. Steganography based techniques have been used in the context of remote authentication to hide biometric feature vectors. Biometric cryptosystems, on the other hand, are proposed to enhance the security of biometric systems and to create revocable representations of individuals. However, neither steganography nor biometric cryptosystems are immune against replay attack and other remote attacks. This paper proposes a novel approach that combines steganography with biometric cryptosystems effectively to establish robust remote mutual authentication between two parties as well as key exchange that facilitates one-time stego-keys. The proposal involves the use of random orthonormal projection and multifactor biometric key binding techniques, and relies on a mutual challenge/response and one-time stego-keys to prevent replay attacks and provide non-repudiation feature. Implementation details and simulation results based on face biometric show the viability of our proposal. Furthermore, we argue that the proposed scheme enhances security while it can be both user-friendly and cost-effective.

Mutual authentication

10.1109/icitst.2013.6750224

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Citations (9)

Topological data analysis as image steganalysis technique

Aras Asaad Sabah Jassim Rasber Dh. Rashid

Image Steganography is the technique of hiding sensitive data (secrete message) inside cover images in a way that no suspicion occurs to attackers, while steganalysis is the technique of detecting the embedded data by unauthorized persons. As a first step of detecting hidden data, distinguishing between original (Images without secrete message) and Stego (Images contain secrete message) is important. In this paper we design and propose a novel scheme based on the emerging field of Topological Data Analysis (TDA) concept of persistent homological (PH) invariants (e.g. No. of connected components), associated with certain image features. Selected group of Uniform Local Binary Pattern (LBP), which is a texture descriptor, codes representing the image features used to construct a sequence of simplicial complexes (SC) from an increasing sequence of distance thresholds (T). We calculate the corresponding non-increasing sequence of homological invariants which shows the speed at which the constructed sequence of SCs terminates. This approach is sensitive to differentiate original images from stego images. We test this approach on three different embedding techniques which are Traditional Least Significant Bits (TLSB) embedding technique, spatial Universal Wavelet Relative Distortion (S-UNIWARD) and LSB-Witness embedding technique together with a large number of images chosen randomly from large database of images. Preliminary results show that the PH sequence defines a discriminates criterion for steganalysis purpose with over 90% classification accuracy.

Steganalysis

Sequence (biology)

Distortion (music)

10.1117/12.2309767

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Citations (3)

Cover image selection for embedding based on different criteria

Rasber Dh. Rashid

The main goal of image steganography techniques is to maximize embedding rate while minimizing the change of the cover image after embedding. Much work has been done on how sender would embed the secret message in the cover image (i.e. embedding techniques) but there is a few works focus on how the senders choose the cover images. One advantage of image steganography is that the cover image only acts as a carrier for the message and the embedder (sender) has the freedom to choose a cover image amongst a set of cover images those results in the least detectable stego image. The way of choosing the cover image is important and since it is available to the sender both the cover and stego images, then the senders are able to measure the embedding artifacts directly. Thus, we are interested in measures which are able to quantify such artifacts. We can use the cover-stego based on measures which we have employed in our work to select best cover image among set of images. The measures used are (1) Number of Modifications to the cover image could be thought as the most intuitive. The smaller the number of changes made the less detectable the resulting stego image should be, (2) Peak Signal to Noise Ratio (PSNR) which is obtained from the cover-stego image pairs where higher PSNR values are assumed to be indicative of lesser delectability, or (3) Based on the robustness to the steganalysis techniques. For the experiments we used a dataset of gray scale images with size of 512×512 resolutions as a cover image with different secret message size from 0.2 to 1.0 bits per pixel.

Steganalysis

Communication source

Robustness

10.1117/12.2560720

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Citations (2)

Topological image texture analysis for quality assessment

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2017)

Aras Asaad Rasber Dh. Rashid Sabah Jassim

Image quality is a major factor influencing pattern recognition accuracy and help detect image tampering for forensics. We are concerned with investigating topological image texture analysis techniques to assess different type of degradation. We use Local Binary Pattern (LBP) as a texture feature descriptor. For any image construct simplicial complexes for selected groups of uniform LBP bins and calculate persistent homology invariants (e.g. number of connected components). We investigated image quality discriminating characteristics of these simplicial complexes by computing these models for a large dataset of face images that are affected by the presence of shadows as a result of variation in illumination conditions. Our tests demonstrate that for specific uniform LBP patterns, the number of connected component not only distinguish between different levels of shadow effects but also help detect the infected regions as well.

Local Binary Patterns

Persistent Homology

Connected component

Texture (cosmology)

Feature (linguistics)

10.1117/12.2268471

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Citations (4)

Edge Based Image Steganography: Problems and Solution

Rasber Dh. Rashid Taban Majeed

Steganography means hiding secrete message in cover object in a way that no suspicious from the attackers, the most popular steganography schemes is image steganography. A very common questions that asked in the field are: 1- what is the embedding scheme used?, 2- where is (location) the secrete messages are embedded?, and 3- how the sender will tell the receiver about the locations of the secrete message?. Here in this paper we are deal with and aimed to answer questions number 2 and 3. We used the popular scheme in image steganography which is least significant bits for embedding in edges positions in color images. After we separate the color images into its components Red, Green, and Blue, then we used one of the components as an index to find the edges, while other one or two components used for embedding purpose. Using this technique we will guarantee the same number and positions of edges before and after embedding scheme, therefore we are guaranteed extracting the secrete message as it's without any loss of secrete messages bits.

Communication source

Steganography tools

10.1109/iccspa.2019.8713712

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Citations (18)

Multi-Edge Concept used for Image Steganography

Innovaciencia Facultad de Ciencias Exactas Físicas y Naturales (2019)

Rasber Dh. Rashid Ladeh S. Abdulrahman Taban Majeed

Digital Steganography means hiding sensitive data inside a cover object ina way that is invisible to un-authorized persons. Many proposed steganography techniques in spatial domain may achieve high invisibility requirement but sacrifice the good robustness against attacks. In some cases, weneed to take in account not just the invisibility but also we need to thinkabout other requirement which is the robustness of recovering the embedded secrete messages. In this paper we propose a new steganoraphicscheme that aims to achieve the robustness even the stego image attackedby steganalyzers. Furthermore, we proposed a scheme which is more robust against JPEG compression attack compared with other traditionalsteganography schemes.

Invisibility

Robustness

Steganography tools

JPEG

10.15649/2346075x.691

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Citations (1)

Persistent Homology for Breast Tumor Classification using Mammogram Scans

arXiv (Cornell University) (2022)

Aras Asaad Dashti Ali Taban Majeed Rasber Dh. Rashid

An Important tool in the field topological data analysis is known as persistent Homology (PH) which is used to encode abstract representation of the homology of data at different resolutions in the form of persistence diagram (PD). In this work we build more than one PD representation of a single image based on a landmark selection method, known as local binary patterns, that encode different types of local textures from images. We employed different PD vectorizations using persistence landscapes, persistence images, persistence binning (Betti Curve) and statistics. We tested the effectiveness of proposed landmark based PH on two publicly available breast abnormality detection datasets using mammogram scans. Sensitivity of landmark based PH obtained is over 90% in both datasets for the detection of abnormal breast scans. Finally, experimental results give new insights on using different types of PD vectorizations which help in utilising PH in conjunction with machine learning classifiers.

Persistent Homology

ENCODE

Landmark

Local Binary Patterns

Representation

10.48550/arxiv.2201.02295

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