Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology

Abstract

The Chinese art market fostered the advancement of culture and spirituality. Nevertheless, false and counterfeit artwork is not uncommon. The art technology and security introduce the innovative method to determine Artwork Traceability and Anti-Counterfeiting. The aim of the work is to prevent the creation and circulation of fake or unauthorized copies of artworks. Because of the cryptographic security and immutability of block chains, it would be challenging to falsify artwork records or make accurate duplicates. This would foster confidence in the art market and shield collectors and artists from monetary losses and harm to their reputations. The data on mobile Application Data Center (ADC) platform and the cleansing the data using sub-aperture keystone transform matched filtering (SAKTMF) and the preprocessed data is fed into the Hierarchically Gated Recurrent Neural Network (HGRNN) classify the art work and Anti-Counterfeiting. The Bald Eagle Search Optimization (BES) Algorithm is used to optimize the weight parameter of HGRNN. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Dual-Branch Multi-Scale Feature Fusion network (DMF-Net), Region Convolutional Neural Network (R-CNN) and practical Byzantine fault tolerance (PBFT) algorithm. The gained results of the proposed HGRNN-BES method attains higher accuracy 97%, 96%, and 99%, higher precision 98%, 99%, and 97%.