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.
Chhavi Bajpai1, Manish Gaur2, Gajendrasinh N. Mori3, Palak Keshwani4
Dr. A. P. J. Abdul Kalam Technical University, India1,2, The Mandvi Education Society Technical Campus, India3, ICFAI Foundation for Higher Education, India4
Deep Learning, Watermarking, Robustness, CNN, Multimedia Security
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 1 , Pages: 3375 - 3378 )
Date of Publication :
August 2024
Page Views :
23
Full Text Views :
3
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