<p>The rapid advancement of multimedia and network technologies has significantly simplified access to digital media. Watermarking techniques are essential for protecting digital images. The growing need to protect intellectual property has increased the demand for effective image watermarking, but existing approaches often struggle with challenges related to robustness and transparency. In this manuscript, Binarized Simplicial Convolutional Neural Networks for Secure and Robust Digital Image Watermark Embedding Using Elite opposite Sparrow Search Optimization Approach (BSCNN-SRDIW-EOSOA) is proposed. Input images are collected from COCO 2017 Dataset. The collected images are pre-processed utilizing the Multi Window Savitzky Golay Filter (MWSGF) to remove the noises and increase the quality of input images. The preprocessed images are fed into feature extraction using the Holistic Dynamic Frequency Transformer (HDFT) to extract the texture features like homogeneity, contrast, correlation, entropy. The extracted features are embedded using the Binarized Simplicial Convolutional Neural Networks for digital image watermark embedding. To enhance accuracy, the Elite opposite Sparrow Search Optimization Algorithm (EOSOA) is utilized to optimize BSCNN parameters, ensuring precise digital image watermark embedding. The proposed BSCNN-SRDIW-EOSOA method is simulated and performance metrics like Normalized correlation (NC), Bit Error Rate (BER), Peak signal-to-noise ratio (PSNR), Structural Similarity Index Measure (SSIM) and Embedding Efficiency are examined. The performance of the proposed BSCNN-SRDIW-EOSOA method attains 35.66%, 32.73%, and 31.43% higher PSNR 32.77%, and 28.93% higher SSIM 24.54%, 23.65%, and 23.62% higher NC is compared with existing DIW-CNN-MMTA, WCFO-DCNN-IW and DIW-DNN-MTA techniques respectively.</p>