In response to the growing need for practical and scalable methods of protecting digital imagery, we propose a novel end-to-end approach based on transparent, zero-bit watermarking. Our method enables not only the detection and localization of visual manipulations but also the classification of attack types, with explicit sup-port for both signal-level perturbations and semantically meaningful edits. The system architecture comprises four specialized mod-ules: a semi-fragile encoder, a decoder for tamper localization, a binary manipulation detector, and a multi-label attack classifier. Designed with modularity and resolution-agnostic processing in mind, the pipeline supports input images of arbitrary size, including ultra high-resolution formats up to 8K. Experimental results on a custom dataset of over 304,000 images show that the proposed solution achieves high visual transparency(PSNR around 43 dB and SSIM close to 0.994), strong localization accuracy, and precise classification performance across a wide range of attacks. The system remains computationally efficient and can operate in reasonable time on standard CPUs without requiring dedicated GPU acceleration. The proposed framework demonstrates a balanced trade-off between robustness, interpretability, and deployment practicality, offering a viable solution for real-world image integrity verification.
Duszejko et al. (Wed,) studied this question.