The widespread use of face retouching filters on short-video platforms has
raised concerns about the authenticity of digital appearances and the impact of
deceptive advertising. To address these issues, there is a pressing need to
develop advanced face retouching techniques. However, the lack of large-scale
and fine-grained face retouching datasets has been a major obstacle to progress
in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and
fine-grained face retouching dataset that contains over half a million
conditionally-retouched images. RetouchingFFHQ stands out from previous
datasets due to its large scale, high quality, fine-grainedness, and
customization. By including four typical types of face retouching operations
and different retouching levels, we extend the binary face retouching detection
into a fine-grained, multi-retouching type, and multi-retouching level
estimation problem. Additionally, we propose a Multi-granularity Attention
Module (MAM) as a plugin for CNN backbones for enhanced cross-scale
representation learning. Extensive experiments using different baselines as
well as our proposed method on RetouchingFFHQ show decent performance on face
retouching detection. With the proposed new dataset, we believe there is great
potential for future work to tackle the challenging problem of real-world
fine-grained face retouching detection.

本研究提出了一个大规模的精细化人脸修饰数据集 RetouchingFFHQ，其中包含超过 50 万幅条件化修饰的图像。通过包含四种典型的人脸修饰操作和不同的修饰水平，将二元人脸修饰检测扩展为多粒度、多修饰类型和多修饰水平的问题。此外，我们提出了一种多粒度注意力模块 (MAM) 作为 CNN 主干的插件，以增强跨尺度的表示学习。通过对 RetouchingFFHQ 数据集进行广泛的实验，包括使用不同基线方法和我们提出的方法，在人脸修饰检测方面展现了良好的性能。在提出的新数据集的基础上，我们相信未来有很大的潜力来解决现实世界中细粒度的人脸修饰检测问题。