Harnessing Convolutional Neural Networks for Robust Digital Image Watermarking

Harnessing Convolutional Neural Networks for Robust Digital Image Watermarking

Authors- Navaneedha Rishnan K, Professor DR.S.Nagasundaram

Abstract--In the era of digital communication and multimedia sharing, ensuring the integrity and ownership of digital content has become increasingly crucial. Digital watermarking techniques offer a solution by embedding imperceptible yet detectable signals within multimedia content, serving as a form of copyright protection and authentication. This research presents a novel approach to digital watermarking using convolutional neural networks (CNNs). The proposed technique involves the training of a CNN model to embed binary watermarks into images, followed by a demodulation and extraction process to recover the watermark from watermarked images. Evaluation metrics such as Bit Error Rate (BER), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) are employed to assess the fidelity of the watermarked images and the accuracy of watermark extraction. Comparative analysis with traditional watermarking techniques demonstrates the effectiveness of the CNN-based approach in terms of robustness and imperceptibility. The results showcase the potential of CNNs in enhancing the security and authenticity of digital content through watermarking, paving the way for advanced applications in digital rights management and content authentication.

DOI: /10.61463/ijset.vol.13.issue3.146