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    Efficient invisible color image watermarking based on chaos
    (Institute of Advanced Engineering and Science (IAES), 2024) Belkacem, Samia; Messaoudi, Noureddine
    Several difficulties are faced in developing a robust and transparent color image watermarking system, which requires the blending of the human visual system (HVS) during its design. Therefore, employing masks that take into account the features of HVSs has become a very effective tool for boosting robustness requirements without significant alterations in image imperceptibility. The present article offers watermarking strategy for colored images employing a reverse self-reference image in conjunction with the HVS constraint. A color image first undergoes conversion through the Red, Green, and Blue (RGB) format to the National Television Systems Committee (NTSC) space. The reference image is derived from the luminance channel through the discrete wavelet transform (DWT) domain. However, the chaotic map serves to generate the watermark, and a 2D torus automorphism is subsequently used to scramble the watermark. Therefore, the watermark is scrambled and placed in the reference image. Moreover, the detecting phase involves the host image, where the reference image is extracted from both the host and the image with a watermark, and the correlation is subsequently used to assess the similarity between the retrieved and the introduced watermark. The proposed watermarking scheme can retain the watermarked image's perceptibility justified by the PSNR. In addition, it achieves high robustness to withstand a wide array of attacks.
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    Big data clustering based on spark chaotic improved particle swarm optimization
    (Institute of Advanced Engineering and Science (IAES), 2024) Boushaki, Saida Ishak; Mahammed, Brahim Hadj; Bendjeghaba, Omar; Mosbah, Messaoud
    In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.