Explainability-Driven Incremental Image Anonymization

Identificador:  imarina:9466969

Handle: https://hdl.handle.net/20.500.11797/imarina9466969

Autores:  Haffar, R; Sanchez, D; Khan, Y; Domingo-Ferrer, J

Resumen:
Privacy regulations require that images depicting humans be anonymized before they are publicly released or shared for secondary use. However, current image anonymization methods significantly degrade the analytical utility of protected images. This paper addresses the challenge of balancing privacy protection and utility preservation in image anonymization. We propose a general disclosure risk-aware anonymization framework that leverages explainability techniques to target identity-revealing features in images. Contrary to conventional methods, which uniformly perturb all image pixels, our proposal focuses on perturbing the pixels that contribute most to disclosure. Moreover, pixel perturbation is enforced incrementally and it is driven by the observed residual risk. Our framework is not tied to a specific pixel perturbation mechanism, and is versatile enough to support a wide variety of techniques, including blurring, pixelation, noise addition and pixel masking. Empirical results show that even with the simplest perturbation techniques, our approach significantly improves the privacy/utility trade-off compared to conventional and advanced state-of-the-art methods.