This work addresses how to validate group fairness in image recognition software. We propose a distribution-aware fairness testing approach (called DistroFair) that systematically exposes class-level fairness violations in image classifiers via a synergistic combination of out-of-distribution (OOD) testing and semantic-preserving image mutation. DistroFair automatically learns the distribution (e.g., number/orientation) of objects in a set of images. Then it systematically mutates objects in the images to become OOD using three semantic-preserving image mutations -- object deletion, object insertion and object rotation. We evaluate DistroFair using two well-known datasets (CityScapes and MS-COCO) and three major, commercial image recognition software (namely, Amazon Rekognition, Google Cloud Vision and Azure Computer Vision). Results show that about 21% of images generated by DistroFair reveal class-level fairness violations using either ground truth or metamorphic oracles. DistroFair is up to 2.3x more effective than two main baselines, i.e., (a) an approach which focuses on generating images only within the distribution (ID) and (b) fairness analysis using only the original image dataset. We further observed that DistroFair is efficient, it generates 460 images per hour, on average. Finally, we evaluate the semantic validity of our approach via a user study with 81 participants, using 30 real images and 30 corresponding mutated images generated by DistroFair. We found that images generated by DistroFair are 80% as realistic as real-world images.

翻译：本文旨在解决图像识别软件中群体公平性的验证问题。我们提出了一种面向分布感知的公平性测试方法（称为DistroFair），该方法通过结合分布外（OOD）测试与语义保持图像变异，系统地揭示图像分类器中类级别的公平性违规。DistroFair自动学习一组图像中对象的分布（例如数量/方向），然后通过三种语义保持的图像变异操作——对象删除、对象插入和对象旋转——系统地变异图像中的对象使其成为OOD样本。我们使用两个知名数据集（CityScapes和MS-COCO）以及三款主流商业图像识别软件（即Amazon Rekognition、Google Cloud Vision和Azure Computer Vision）对DistroFair进行评估。结果表明，DistroFair生成的图像中约有21%通过真实标签或蜕变测试预言暴露了类级别公平性违规。DistroFair的有效性比两种主要基线方法高出2.3倍：即（a）仅关注生成分布内（ID）图像的方法，以及（b）仅使用原始图像数据集进行公平性分析的方法。我们进一步观察到DistroFair具有高效性，平均每小时可生成460张图像。最后，我们通过一项包含81名参与者的用户研究（使用30张真实图像及DistroFair生成的30张对应变异图像）评估了该方法的语义有效性。研究发现，DistroFair生成的图像在真实感方面达到了真实世界图像的80%。