Secure Synthetic Image Generation Using Self-Attention GAN and Blockchain

Privacy concerns are paramount in today’s digital era, particularly in AI-driven environments. Traditional privacy preservation approaches are inadequate due to their reliance on outdated methods, highlighting the need for innovative solutions to ensure robust security and confidentiality. This paper proposes a robust AI framework, Secure Self-Attention GAN (SSA-GAN), to safeguard image data. Our methodology combines advanced clustering algorithms and blockchain technology to ensure privacy and security. SSA-GAN generates highly realistic synthetic face images, while mini-batch K-means clustering groups them for enhanced privacy. Blockchain integration provides a transparent and secure storage solution. We evaluate the proposed framework on VGGFace2 datasets, achieving outstanding results: an Inception-Score(IS) of 12.49 and a Fréchet Inception Distance(FID) of 29.70, displayed significant improvements over baseline approaches. Our clustering approach successfully identifies unique facial attribute clusters, and blockchain integration maintains data privacy and security with 87% precision, 82% recall, 84% F1-score, and 92% ROC accuracy. This framework advances the state of the art in privacy-preserving AI, enabling critical applications like identity verification and medical image management.