Abstract
Bias analysis for synthetic face detection is bound to become a critical topic in the coming years. Although many detection models have been developed and several datasets have been released to reliably identify synthetic content, one crucial aspect has been largely overlooked: these models and training datasets can be biased, leading to failures in detection for certain demographic groups and raising significant social, legal, and ethical issues. In this work, we introduce an evaluation framework to contribute to the analysis of bias of synthetic face detectors with respect to several facial attributes. This framework exploits synthetic data generation, with evenly distributed attribute labels, for mitigating any skew in the data that could otherwise influence the outcomes of bias analysis. We build on the proposed framework to provide an extensive case study of the bias level of five state-of-the-art detectors in synthetic datasets with 25 controlled facial attributes. While the results confirm that, in general, synthetic face detectors are biased towards the presence/absence of specific facial attributes, our study also sheds light on the origins of the observed bias through the analysis of the correlations with the balancing of facial attributes in the training sets of the detectors, and the analysis of detectors activation maps in image pairs with controlled attribute modifications.
Abstract (translated)
合成面部检测中的偏见分析必将成为未来几年的一个关键议题。尽管已经开发了许多检测模型，并且发布了几个可靠地识别合成内容的数据集，但一个重要的方面被广泛忽视了：这些模型和训练数据集可能会存在偏差，导致某些人口群体的检测失败，并引发重大的社会、法律和伦理问题。在本项工作中，我们介绍了一个评估框架，旨在对五种最先进的合成面部探测器相对于多种面部属性进行偏见水平分析。该框架利用具有均匀分布属性标签的合成数据生成技术来缓解可能影响偏见分析结果的数据偏差。 基于提出的框架，我们在包含25个受控面部属性的合成数据集中进行了详尽案例研究，以评估五种最先进的检测模型的偏见程度。虽然结果显示，总的来说，合成面部探测器对特定面部特征的存在或缺失存在偏向性，但我们的研究还通过分析训练集中的面部属性平衡以及在具有控制属性修改的图像对中探测器激活图的变化，揭示了观察到的偏见的起源。 这项工作强调了需要更加关注和理解合成检测模型中存在的潜在偏差，并为未来的研究提供了重要的见解，以开发更公平、透明且可靠的技术。
URL
https://arxiv.org/abs/2507.19705
