AURA-Net: A Deep Learning Framework for Robust Authorship Verification in Print-and-Capture Resistant Image Watermarking

The exponential growth of digital imagery across online and printed media has intensified the need for reliable authorship verification systems. Ensuring the integrity of embedded watermarks becomes especially challenging under print-and-capture (P&C) conditions, where halftoning, optical blur, illumination variation, and sensor noise jointly distort the embedded signal. To address these challenges, this work presents AURA-Net, a deep learning-based watermarking framework capable of embedding and reliably recovering a fixed 88-bit watermark after full physical reproduction. The system incorporates a dual-head U-Net architecture that jointly predicts the embedded bitstream and estimates a presence confidence score. A confidence threshold (τ = 0.76), together with a bounded retry-decoding mechanism, ensures robust recovery even under difficult lighting and capture conditions. In addition, we introduce a mathematical formulation of the P&C process and an analytical derivation of AURA-Net’s robustness, including a linearized P&C degradation model, latent-space margin separation, likelihood-based presence detection, and cumulative reliability under retry decoding. The model is trained using progressive clean masking and photometric distortions (JPEG compression, Gaussian blur, histogram variation, and synthetic noise) to improve generalization. Experiments on 200 physically captured samples demonstrate that AURA-Net achieves a mean bitwise accuracy of 99.8% in good illumination (μ = 0.801) and maintains a confidence of μ = 0.745 in low-light settings, where recovery is stabilized through retry decoding. Under purely digital attacks, the model yields 40.91-44.32% bitwise accuracy across JPEG compression, blur, salt-and-pepper noise, and speckle noise, reflecting a practical trade-off due to its optimization for physical robustness. High imperceptibility is consistently maintained, with an SSIM of 0.997 and negligible grayscale histogram deviation. AURA-Net executes embedding and extraction in 0.003 s and 0.002 s, respectively, enabling real-time deployment. Overall, the proposed framework demonstrates reliable watermark preservation across both digital and physical domains, establishing a reproducible and analytically grounded foundation for post-print authorship verification.