College News

Pilot competitive examination for applicants to graduate studies for the academic year (2022-2023)

Pilot competitive examination for applicants to graduate studies for the academic year (2022-2023)   2022-05-11   

 Conducting a pilot competitive examination for applicants for graduate studies in the College of Computer Science and Information Technology / University of Anbar for the academic year 2022-2023

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Academic article by Assistant Professor Dr. Ismail Taha AhmedCommon Gabor Features for Image Watermarking Identification Assist Prof Dr. Ismail Taha Ahmed Abstract Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark. Keywords watermarking identification; Gabor feature; discriminant analysis (DA) classifier; Random_forest classifier

Academic article by Assistant Professor Dr. Ismail Taha AhmedCommon Gabor Features for Image Watermarking Identification Assist Prof Dr. Ismail Taha Ahmed Abstract Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark. Keywords watermarking identification; Gabor feature; discriminant analysis (DA) classifier; Random_forest classifier   2022-04-04   

 Common Gabor Features for Image Watermarking Identification

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Discussion of a master's thesis in the College of Computer Science and Information Technology

Discussion of a master's thesis in the College of Computer Science and Information Technology   2022-03-14   

Scientific discussion for a master's student (Hamsa Mohamed Ahmed)

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Final exams - first stage

Final exams - first stage   2022-03-14   

The start of the exams for the first stage of the first semester in the Faculty of Computer Science and Information Technology for the academic year 2021/2022

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Curriculum guide at the University of Anbar for the year 2021.

Curriculum guide at the University of Anbar for the year 2021.   2022-03-08   

completing the evidence that would inform students, researchers

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