An adaptive ACM watermarking technique based on combined feature extraction and non-linear equation

Digital watermarking plays a vital role in safeguarding data integrity and intellectual property, particularly in medical imaging, where ensuring authenticity and preserving privacy are paramount. Embedding hidden information within images aids authentication, copyright protection, and integrity verification. This work proposes a robust palmprint-based watermarking approach based on adaptive ACM watermarking with a non-linear equation technique to enhance patient security. The proposed method integrates local binary patterns and histograms of oriented gradients for robust feature extraction, discrete wavelet transforms for multi-level image decomposition, Arnold Cat Map for chaotic mapping, and singular value decomposition for stable watermark embedding. The contribution goals are improving security, imperceptibility, and resilience against image processing attacks. Experimental results demonstrate the method's effectiveness, with a peak signal-to-noise ratio of 63.52 and a structural similarity index of 1.00, indicating high image quality retention. An equal error rate of 0.035 confirms the method's reliability in watermark detection. These findings underscore the proposed method's suitability for secure medical image watermarking applications, enhancing data integrity and patient privacy.