VerA: Versatile Anonymization Fit for Clinical Facial Images
Majed El Helou Doruk Cetin Petar Stamenkovic Fabio Zund
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.