A watermarking field is necessary to prove the copyright, ownership, authenticity and other related security aspects of the electronic data. Semi-Blind watermarking refers to need the watermark image during the extraction process. While, informed watermarking generates the watermark data from the original image itself. Moreover, Tamper detection is useful to discover the tamper zone in the image. In this paper, we propose a semi blind and informed watermarking approach. We build the watermark from the original image using Weber Law. Our approach aims to provide a high robustness and imperceptibility with perfectly tamper detection zone. We divide the original image into blocks and the main pixel is chosen for watermark insertion, where the embedding/extraction operates in the spatial domain. The tamper detection is tested by tampering watermarked image, and then based on the extracted attacked watermark; we can discover the tamper area. Also, the robustness watermarking aspect is proved against different kind of geometric and non-geometric attacks. Based on the experimental results, the imperceptibility and robustness of our watermarking approach are proven and showing perfectly the detection of the tamper zones.

Diabetic retinopathy, a common complication of diabetes, is further exacerbated by factors such as hypertension and obesity. This study introduces the Diabetic Retinopathy Compact Convolutional Transformer (DRCCT) model, which combines convolutional and transformer techniques to enhance the classification of retinal images. The DRCCT model achieved an impressive average F1-score of 0.97, reflecting its high accuracy in detecting true positives while minimizing false positives. Over 100 training epochs, the model demonstrated outstanding generalization capabilities, achieving a remarkable training accuracy of 99% and a validation accuracy of 95%. This consistent improvement underscores the model’s robust learning process and its effectiveness in avoiding overfitting. On a newly evaluated dataset, the model attained precision and recall scores of 96.93% and 98.89%, respectively, indicating a well-balanced handling of false positives and false negatives. The model’s ability to classify retinal images into five distinct diabetic retinopathy categories demonstrates its potential to significantly improve automated diagnosis and aid in clinical decision-making.

A hybrid watermarking approach for DICOM medical image protection is proposed in this paper. The medical image is first separated into two parts: the region used for the diagnosis, called Region Of Interest (ROI), and the remaining, called Region Of Non-Interest (RONI). The patient’s name is then extracted from the header of the DICOM image and pertinent features from the ROI. These data are therefore used to construct a watermark based on a Jacobian model. On the one hand, this watermark is used for the zero watermarking of the ROI. On the other hand, it is divided into blocks that are embedded in the RONI using a linear interpolation technique. Experiment results show a good performance of the proposed approach. The average values of SSIM, PSNR, NC, and BER are respectively 0.98, 71 dB, 0.98, and 0.0054, and the watermark embedding and extraction times do not exceed 12 s.