DEEPMARKNET FOR ROBUST IMAGE AND VIDEO WATERMARKING EMBEDDING AND DETECTION

ICTACT Journal on Image and Video Processing ( Volume: 15 , Issue: 1 )

Abstract

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.

Authors

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 University, Raipur, India4

Keywords

Deep Learning, Watermarking, Robustness, CNN, Multimedia Security

Published By
ICTACT
Published In
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 1 )
Date of Publication
August 2024
Pages
3375 - 3378

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