Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2114 2019 2019 Department(Computer Science &Engineering ) Shobhit Institute of Engineering and Technology Meerut, Uttar Pradesh, India Aditi Aditi Department(Computer Science &Engineering ) Shobhit Institute of Engineering and Technology Meerut, Uttar Pradesh, India R. P. Aggarwal Department (Computer Application ) Ajay Kumar Garg Engineering College, Ghaziabad (AKTU, Lucknow ) Ghaziabad, Uttar Pradesh, India B. K. Sharma Department of Computer Science and Engineering, Parul Institute of Technology, Gujarat,India; IEEE Senior Member, Parul University, India Aditi Sharma Image watermarking preserves digital content. This study introduces a new watermarking approach employing Sub-Band Discrete Cosine Transform and Deep neural networks, GRNN and CNN. The method embeds robust, invisible watermarks in greyscale photos and compares the two neural network topologies. The watermark is added using sub-band DCT. Watermark embedding in low-frequency sub-bands resists photo processing. The binary watermark modifies sub-band DCT coefficients to determine embedding intensity, resisting signal deterioration, and assaults. GRNN and CNN neural networks extract watermarks accurately. CNN extracts hierarchical features from images, enabling robust watermark recovery even under distortions, whereas non-parametric GRNN stores the whole training data to create predictions. The watermarking approach is tested on several greyscale photos. PSNR, SSIM, MSE, and NCC measure performance. The watermark tests noise addition, compression, and filtering. Compare GRNN and CNN's watermark extraction strengths and shortcomings to assess image watermarking suitability. 2023 2023 33 47 10.54216/JISIoT.100103 https://www.americaspg.com/articleinfo/18/show/2114