College of Computer Science and Mathematics

Discussion of a Doctorial Dissertation  in the College of Computer Science and Mathematics – Department of Mathematics Sciences entitled:

“ Improved Chaotic Intelligent Numerical Optimization Algorithms for Optimizing of Information Steganography “

 In continuation of the scientific research movement and with the follow-up and presence of the Dean of the College of Computer Science and Mathematics

   Prof. Dr. Duha Bashir Abdullah The esteemed

It was discussed in the discussion room at the Faculty of Computer Science and Mathematics at the University of Mosul on Wednesday  1-11-2023

Doctorial Dissertation by student Yahya Abdul fattah Hamoodi

, under the supervision of    Prof. Dr. Ban Ahmed Hassan

Artificial algorithms are regarded as one of the most important drivers of technological advancement at present. They rely on concepts and techniques inspired by human thinking and natural intelligence. Among the prominent domains of artificial algorithms is their orientation towards information security. Information has acquired significant value, necessitating protection and security measures that go beyond traditional safeguards. The concept of information security holds great importance in various aspects of life, whether within large institutions or at the individual level. Modern studies aim to preserve the confidentiality and integrity of information and personal data. This research presents trends in intelligent algorithms that aim to clandestinely conceal information using the technique of secret hiding.

In the first route, algorithms like (AO), (CSA), and (POA) are improved by employing Chaotic Maps (CH) to generate new algorithms (CH-AO), (CH-CSA), and (CH-POA). This is accomplished by changing important algorithm parameters and using the Chaotic Map (CH) to balance the unpredictability built into the algorithms in an effort to get better outcomes.

The second approach utilizes hybridization techniques to enhance the aforementioned Chaotic Algorithms using the Artificial Hummingbird Algorithm (AHA):

• The first method involves utilizing equations in the optimization process.
• The second method involves employing communities of solutions for the optimization process.

This optimization has been applied to the following algorithms:

The first algorithm constructs a new algorithm known as (CH-POA-AHA) by fusing the Chaotic Peafowl Algorithm (CH-POA) and the Artificial Hummingbird Algorithm (AHA).

The second algorithm establishes an entirely novel, intelligent algorithm called (CH-CSA-AHA) through the combination of the Chaotic Chameleon Algorithm (CH-CSA) and the Artificial Hummingbird Algorithm (AHA).

The third algorithm creates a new algorithm termed (CH-AO-AHA) by combining the Chaotic Aquila Algorithm (CH-AO) and the Artificial Hummingbird Algorithm (AHA).

The indicated trio of algorithms in this study demonstrate that they are effective in improving results and enhancing solution accuracy, hence reducing the time needed to arrive at optimal solutions. This validates the algorithms’ utility.

The third direction in this research involves designing intelligent algorithms specialized in secret concealment based on the aforementioned hybrid algorithms. Their core function is to encrypt the text and then hide it within an image. This process entails selecting optimal locations within the image and choosing the best decryption keys after extracting the text from the image.

Finally, the outcomes showed that great concealing accuracy had been achieved, with no evidence of visual distortion or deterioration following the concealment procedure. The text was correctly retrieved and had no mistakes or omissions. Peak Signal-to-Noise Ratio (PSNR), a metric for evaluating the quality of the concealed picture, and Mean Squared Error (MSE), a measure of the accuracy of the findings, were used to determine the degree of concealment.

The scientific committee included the following members:

• Dr. Bassim Abbas Hassan – Chairman .
• Dr. Omar Saber Qasim – Member.
• Dr. Hamsa Tharwat Saeed – Member.
• Prof. Dr. Ahmed Sami Noori – Member.
• Prof. Dr Nizar Khalaf Hussein – Member
• Prof. Dr. Ban Ahmed Hassan – Member and Supervisor