Generation of Synthetic Datasets using Generative Adversarial Networks and Securing it using Blockchain

Emerging from the challenges of generating secure synthetic image datasets, this research is motivated by the aim to elevate dataset quality while upholding data security. Synthetic datasets play a crucial role in training machine learning models, but their quality and diversity are often limited. Simultaneously, the security and authenticity of these datasets become paramount in an era of increasing data breaches and fraudulent activities. This study offers a comprehensive solution to two important challenges using advanced technologies. Firstly, we focus on creating diverse synthetic images that help improve the training of artificial intelligence models. Secondly, we ensure the safety of these images through a technology called blockchain. By combining these two technologies, we generate images and make sure they can't be tampered with. We use a method called Generative Adversarial Networks for generating images and then store them securely on a blockchain. Our project approach shows that this approach works well in producing images and keeping them safe. This research contributes to the field of AI by addressing the issues of dataset quality and security.