Sandeep Kautish

Video watermarking is a technique used to embed information into video content, ensuring privacy protection and preventing data misuse. However, watermarking schemes that rely on a single transform method are often vulnerable to noise and geometric distortions. To overcome these limitations, this paper introduces a hybrid approach that combines singular value decomposition with discrete wavelet transform and redundant discrete wavelet transform for more effective watermark embedding. The proposed method optimizes the balance between imperceptibility and robustness by fine-tuning the scale factor through a custom fitness function using fruit fly optimization. The watermark is adaptively embedded based on the video’s transform type, sub-band selection, attack types, and keyframes. Block selection within sub-bands is guided by entropy, edge entropy, and contrast. Watermark embedding and extraction are performed on selected blocks that meet these criteria. Experimental results evaluated using metrics such as normalized cross-correlation, image fidelity, and peak signal-to-noise ratio, demonstrate the superiority of our method over state-of-the-art methods.

Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net’s powerful segmentation capabilities with the capsule network’s high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings.