A Self Supervised CNN for Image Watermark

  A Self Supervised CNN for Image Watermark

Kothapally Deepthi  Dept of ECE IARE

Dr. S China Venkateshwarlu Professor Dept of ECE IARE

Dr. V Siva Nagaraju Professor Dept of ECE IARE

Ms.B.Veena ,Assistant Professor Dept of ECE IARE

Abstract -

This project presents a self-supervised convolutional neural network (CNN) framework for robust image watermarking. Unlike traditional methods that rely on labeled data, our approach leverages self-supervised learning to embed and extract watermarks without explicit supervision. The network is trained to encode watermarks in imperceptible ways while maintaining high image quality and resistance to common distortions such as compression and noise. Experimental results demonstrate that our model achieves a strong balance between invisibility, robustness, and watermark retrieval accuracy.

Key Words:   Self-upervisedLearning,ConvolutionalNeuralNetwork(CNN),ImageWatermarking,DigitalWatermark,Robustness,Imperceptibility,Deep Learning,Data Hiding, Watermark Extraction,Image Processing