Synthetic Artwork Authentication Threats: Detection by Combining Neural Network and Blockchain

Abstract

The rapid development of synthetic media tools has blurred the lines between human-created and AI-generated content, which has been exacerbated by overfitted detection models. This has put the authentication of digital media at risk, raising concerns about media credibility and trustworthiness due to the deception presented by synthetic media. Furthermore, a separation between artificial creativity and human creativity means that current ownership laws cannot provide sufficient authentication for digital media. This paper proposes an authentication detection model for artwork by combining a neural network and blockchain technology. Once an artwork has been detected as human-created, its image hash is stored on the blockchain, providing a solution for preserving digital artwork authenticity. The model was trained using a combined dataset composed of both human-created artwork and synthetic artwork generated by the Midjourney and Stable Diffusion tools, resulting in an increase in accuracy of almost 20% for detecting synthetic artwork. By introducing doubt in less confident outputs, the model achieved an accuracy of over 92% when tested against independent datasets. This is a significant improvement over detection models that experience a deterioration in accuracy when faced with independent datasets. Additionally, using the Polygon blockchain instead of Ethereum reduced the time to store authentic artwork on the blockchain from 21 s to 10 s, and the interquartile range of the cost of writing to the blockchain was reduced by 97.4%, improving the scalability of the model. The results of this paper contribute to knowledge by showing how the detection of synthetic artwork can be improved by using multiple datasets for training models, as well as providing long-term preservation of digital artwork authenticity by using blockchain.