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.

翻译：随着面部图像传播中对数据隐私保护的立法要求日益严格，图像匿名化技术的重要性愈发凸显。尽管近期该领域的发展已超越传统像素化或模糊方法，但这些技术主要针对常规单张图像。这导致临床图像匿名化——作为展示医疗干预的必要手段——仍未得到充分解决。我们提出VerA，一种适用于临床面部图像的通用匿名化方法，其特点在于：(1)必须保留特定语义区域以展示医疗干预效果；(2)匿名化图像对对于展示术前术后效果至关重要。在常规图像的脱敏性和逼真度方面，VerA的性能优于或持平于现有最优方法。此外，我们通过广泛的定量与定性评估，验证了该方法在成对图像匿名化以及临床单张与成对图像匿名化中的有效性。