VerA: Versatile Anonymization Fit for Clinical Facial Images

The escalating legislative demand for data privacy in facial image dissemination has underscored the significance of image anonymization. Recent advancements in the field surpass traditional pixelation or blur methods, yet they predominantly address regular single images. This leaves clinical image anonymization -- a necessity for illustrating medical interventions -- largely unaddressed. We present VerA, a versatile facial image anonymization that is fit for clinical facial images where: (1) certain semantic areas must be preserved to show medical intervention results, and (2) anonymizing image pairs is crucial for showing before-and-after results. VerA outperforms or is on par with state-of-the-art methods in de-identification and photorealism for regular images. In addition, we validate our results on paired anonymization, and on the anonymization of both single and paired clinical images with extensive quantitative and qualitative evaluation.