Lightweight adaptive watermarking: a hybrid approach for tamper detection on real-time images using HVS-centric algorithms and convolution attention

Tamper detection is one of the multimedia security issues. Watermarking is an efficient technique for proving ownership, content authentication, copyright protection, and tamper detection. Our objective is to design an adaptable watermarking system that is highly resistant to various attacks. To strike a compromise between imperceptibility and robustness performance against various attacks, the embedding region and embedding strength must be taken as key considerations. Thus, the suggested model uses a novel association rule mining algorithm based on human visual system (HVS) parameters and a modified whale optimization technique to estimate the adaptive embedding region and embedding strength. Along with that, the content authentication data is generated using a transform domain technique, and the tamper localization watermark is generated using the Lightweight CoAtNet model, respectively. For content authentication, the scrambled low-frequency sub-band from the owner's biometric image is obtained using the discrete wavelet transform (DWT) to generate the principal component matrix. To generate the tamper localization watermark, a CoAtNet model extracts robust low-level global features. Prior to embedding, the carrier image is separated into RGB channels and processed using the hybrid Contourlet-DWT framework. Suitable embedding blocks are identified through the HVS-based Apriori algorithm. The selected blocks are further transformed using the Graph Fourier Transform (GFT), where the generated watermark features are embedded. Subsequently, the content authentication watermark is inserted into the DWT mid-frequency sub-band. Finally, the private key is embedded within the detailed contourlet coefficients to produce the copyright-protected watermarked image. Comparative experimental analysis demonstrates that the proposed system achieves superior tamper detectability and enhanced robustness against geometric distortions and multiple signal-processing attacks when compared with existing approaches. Experimental evaluation demonstrates superior imperceptibility and robustness, achieving a maximum PSNR of 64 dB and an NCC of 0.999.