ICTACT Journal on Image and Video Processing

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Deepmarknet for Robust Image and Video Watermarking Embedding and Detection


Affiliations
1 Department of Computer Science and Engineering, Centre for Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, India
2 Department of Computer Science and Engineering, Institute of Engineering and Technology, Dr. A. P. J. Abdul Kalam Technical University, India
3 Department of Master of Computer Applications, The Mandvi Education Society Technical Campus, India
4 Department of Computer Science and Engineering, ICFAI Foundation for Higher Education, India

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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 dualstream 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.

Keywords

Deep Learning, Watermarking, Robustness, CNN, Multimedia Security
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  • Deepmarknet for Robust Image and Video Watermarking Embedding and Detection

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Authors

Chhavi Bajpai
Department of Computer Science and Engineering, Centre for Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, India
Manish Gaur
Department of Computer Science and Engineering, Institute of Engineering and Technology, Dr. A. P. J. Abdul Kalam Technical University, India
Gajendrasinh N. Mori
Department of Master of Computer Applications, The Mandvi Education Society Technical Campus, India
Palak Keshwani
Department of Computer Science and Engineering, ICFAI Foundation for Higher Education, India

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 dualstream 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.

Keywords


Deep Learning, Watermarking, Robustness, CNN, Multimedia Security