Ensuring data protection for eye images by combining fine-tuned image synthesis and anonymity assessment
Abstract
Advances in realistic image synthesis have led to several downstream applications in healthcare, particularly the anonymisation of biometric images. However, generative models pose significant privacy risks when synthetic outputs closely resemble the real, personal images required for training. To address these challenges, we propose a two-stage framework for generating privacy-preserving synthetic eye images based on a dataset of real photographs. Our system consists of a fine-tuned text-to-image synthesiser based on Stable Diffusion (v1-5), followed by a privacy risk scorer termed the "Cone of Privacy" (CoP). Inspired by the DreamBooth method, the synthesiser incorporates information about the original dataset to generate similar realistic yet diverse images. To mitigate identity leakage, the CoP score measures the identifiability of synthetic images with respect to the training images based on embeddings from a pre-trained vision transformer. In evaluations on a dataset of 2,000 images from 704 subjects, our approach successfully generated realistic images while filtering high-risk samples, achieving a privacy threat detection rate above 97%. The Fréchet Inception Distance (FID) between the original and the fully synthetic datasets was 57.1, with privacy-violating synthetic images scoring 51.3 and privacy-preserving images scoring 82.6, demonstrating a trade-off between realism and privacy protection. These results suggest the framework is a promising solution for…
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