The concealment of data has emerged as an area of deep and wide interest in research that endeavours to conceal data in a covert and stealth manner, to avoid detection through the embedment of the secret data into cover images that appear inconspicuous. These cover images may be in the format of images or videos used for concealment of the messages, yet still retaining the quality visually. Over the past ten years, there have been numerous researches on varying steganographic methods related to images, that emphasised on payload and the quality of the image. Nevertheless, a compromise exists between the two indicators and to mediate a more favourable reconciliation for this duo is a daunting and problematic task. Additionally, the current techniques have not been successful in attaining more improved security caused by the non-encrypted data that only underwent the first layer of concealment through merely a straightforward embedment process of the secret data within the images, thus allowing the extraction of the concealed data to be quite simple for hostile entities. Hence, in the current study, the proposed scheme, we have improved the Bit Inverting Map method to narrow the gap of existing work. Our experimental results indicate that the proposed framework maintains a better balance between image visual quality and security, with relatively less computational and complexity, which assures its effectiveness compared to other state-of-the-art methods.
تحلل الورقة الحالية تمثيل كاريل تشرشل للصورة النمطية للمرأة في Top Girls (1982). تُظهر المسرحية كيف وصلت النساء في نضالهن لمحاربة اضطهاد الرجال عبر التاريخ ، إلى مستوى من القوة والحرية يستخدمان للسيطرة على جنسهن دون شفقة. مارلين ، الشخصية المركزية في هذه المسرحية ، هي امرأة تبنت الصفات الذكورية إلى أقصى الحدود. لتسلق سلم النجاح إلى قمته ، تضحي مارلين بطفلها وعائلتها وحبها. كما تعرض المسرحية النساء الصامتات والم
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FG Mohammed, HM Al-Dabbas, Iraqi journal of science, 2018 - Cited by 6
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An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification
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Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.
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This paper presents a proposed method for (CBIR) from using Discrete Cosine Transform with Kekre Wavelet Transform (DCT/KWT), and Daubechies Wavelet Transform with Kekre Wavelet Transform (D4/KWT) to extract features for Distributed Database system where clients/server as a Star topology, client send the query image and server (which has the database) make all the work and then send the retrieval images to the client. A comparison between these two approaches: first DCT compare with DCT/KWT and second D4 compare with D4/KWT are made. The work experimented over the image database of 200 images of 4 categories and the performance of image retrieval with respect to two similarity measures namely Euclidian distance (ED) and sum of absolute diff
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The effect of using three different interpolation methods (nearest neighbour, linear and non-linear) on a 3D sinogram to restore the missing data due to using angular difference greater than 1° (considered as optimum 3D sinogram) is presented. Two reconstruction methods are adopted in this study, the back-projection method and Fourier slice theorem method, from the results the second reconstruction proven to be a promising reconstruction with the linear interpolation method when the angular difference is less than 20°.
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