QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement

Abstract

Artificial intelligence (AI) is of great potential for improving the performance of image processing and applications. In this study, we incorporate two AI techniques, namely, the grey wolf optimizer (GWO) and denoising convolutional neural network (DnCNN), within a framework developed based on the quaternion discrete cosine transform (QDCT). Binary embedding is formulated according to the attribute of each QDCT component and the distinctive properties of available modulation schemes. The GWO is responsible for performance optimization, while the DnCNN makes the extracted binary watermark more visually recognizable. Experiment results demonstrate the efficacy of the proposed scheme for resisting a variety of image processing attacks. The proposed scheme outperforms existing ones in terms of the robustness and intelligibility of the retrieved watermarks under the same payload capacity.

Keywords

• Blind color image watermarking
• grey wolf optimizer
• denoising convolutional neural network
• quaternion discrete cosine transform
• mixed modulation