Exploring Cellular Automata Learning: An Innovative Approach for Secure and Imperceptible Digital Image Watermarking

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data as well as the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behavior of the powerful elementary cellular automata Rule 30. This research is motivated by the potential to incorporate dynamic computational models into the field of image security. We aim to strike a balance between the crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, in the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). In the following, the 2-MSBs watermark is encrypted using an ECA Rule 30 to level up the system’s security attributes. Then, the host image is scrambled using ECA Rule 30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA Rule 30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation’s findings demonstrate strong imperceptibility, as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.