DEEPMARKNET FOR ROBUST IMAGE AND VIDEO WATERMARKING EMBEDDING AND DETECTION
Chhavi Bajpai, Manish Gaur, Gajendrasinh N. Mori, The Mandvi Education Society Technical Campus, India, Palak Keshwani · ICTACT Journal on Image and Video Processing · 2024
In the digital age, securing multimedia content against unauthorized use is critical. Traditional watermarking techniques often struggle with robustness against various attacks. This study introduces a novel DeepMarkNet approach for robust image and video watermarking. DeepMarkNet leverages deep learning to embed and detect watermarks with high resilience to common distortions. The method employs a Convolutional Neural Network (CNN) for embedding and a dual- stream architecture for detection. Experimental results demonstrate DeepMarkNet effectiveness, achieving a 98.5% detection accuracy and maintaining watermark integrity under compression and noise attacks. This outperforms conventional techniques by 15% in robustness.