The demand for privacy in facial image dissemination is gaining ground internationally, echoed by the proliferation of regulations such as GDPR, DPDPA, CCPA, PIPL, and APPI. While recent advances in anonymization surpass pixelation or blur methods, additional constraints to the task pose challenges. Largely unaddressed by current anonymization methods are clinical images and pairs of before-and-after clinical images illustrating facial medical interventions, e.g., facial surgeries or dental procedures. We present VerA, the first Versatile Anonymization framework that solves two challenges in clinical applications: A) it preserves selected semantic areas (e.g., mouth region) to show medical intervention results, that is, anonymization is only applied to the areas outside the preserved area; and B) it produces anonymized images with consistent personal identity across multiple photographs, which is crucial for anonymizing photographs of the same person taken before and after a clinical intervention. We validate our results on both single and paired anonymization of clinical images through extensive quantitative and qualitative evaluation. We also demonstrate that VerA reaches the state of the art on established anonymization tasks, in terms of photorealism and de-identification.

翻译：面部图像传播中的隐私需求在国际上日益受到重视，GDPR、DPDPA、CCPA、PIPL和APPI等法规的激增即是明证。尽管匿名化技术的最新进展已超越像素化或模糊处理方法，但该任务面临的额外约束仍带来挑战。当前匿名化方法大多未能充分处理临床图像以及展示面部医疗干预（如面部手术或牙科治疗）前后对比的临床图像对。我们提出VerA，首个解决临床应用中两大挑战的多功能匿名化框架：A）保留特定语义区域（如口腔区域）以展示医疗干预效果，即仅对保留区域外的部分实施匿名化；B）生成跨多张照片具有一致个人身份的匿名化图像，这对匿名化同一患者在临床干预前后拍摄的照片至关重要。我们通过大量定量与定性评估，在临床图像的单张及配对匿名化任务上验证了方法的有效性。同时证明VerA在现有匿名化任务中，在照片真实性与去身份识别效果方面均达到先进水平。