Robust Watermarking and Image Enhancement Technique Using Classification in Machine Learning

The need for strong watermarking methods to safeguard IP and guarantee authenticity has increased due to the widespread use of digital images across many platforms. At the same time, there has been a meteoric rise in the need for visual quality improvement techniques for images. This work introduces a new method that combines watermarking, picture augmentation, and machine learning classification to provide security, visual integrity, and durability. Common assaults on traditional watermarking systems include cropping, filtering, and compression. On the other hand, our suggested method uses ML classifiers to adaptively embed watermarks in the frequency or spatial domains, making it more resistant to typical assaults. The embedding approach ensures resilience while minimising perceptual distortion by dynamically adapting to picture content features using a classification model trained on a broad dataset. Images that have been watermarked have their visual quality improved since image enhancement is a part of the watermarking framework. The suggested method finds target areas and selectively conducts improvement operations using feature extraction and analysis. This customised method reduces the effect of watermark insertion while preserving crucial picture characteristics.