DBpHash: a blockchain-based dual-band perceptual hashing framework for copyright protection of purely chromatic background images

Pure chromatic background images (PCBIs) consist of large uniform regions with minimal structural content, which makes standard perceptual hashing based on low-frequency DCT unreliable. High similarity scores may be assigned to visually different images, leading to false acceptance. In blockchain-based copyright registration systems, such transactions are irreversible once confirmed, necessitating a conservative similarity model. This paper proposes DBpHash, a dual-band perceptual hashing framework for blockchain-based copyright registration and verification of PCBIs. During computation, low-frequency DCT bands are excluded while mid and high-frequency bands are energy-normalized and binarized to create a 128-bit hash. Similarity is calculated independently for each band and fused using inverse-variance weighting. Thresholds are derived from real and imposter distributions. Experiments conducted on a PCBI dataset are further validated via cross-dataset evaluation on BSDS500 and DTD without retraining. False positive rate remains at 0.095 while similarity exceeds 0.92 under common photometric distortions. Statistical analysis confirms strong hash properties, including near-ideal bit balance and entropy. The framework is integrated with blockchain-based registration and off-chain similarity computation. Results indicate that DBpHash provides a reliable and distortion-resilient solution for copyright authentication of PCBIs.