# Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis
## 4 Experimental Setup and Analysis of Results  
We report the results of our experimental campaign on synthetic and real face images of different sources, and by employing different metrics and feature representations for the joint analysis of \(\mathbf{x_{l}}\) and \(\mathbf{x_{R}}\). In particular, we considered the image data employed for the work [2], which have been made available by the authors ([[https://osf.io/ru36d/](https://osf.io/ru36d/)]([https://osf.io/ru36d/](https://osf.io/ru36d/)) (accessed on 30 December 2022)). They include synthetic images generated through StyleGAN2 (indicated as _SG2_), real images extracted from FFHQ (indicated as _FFHQ_), and the high-quality image dataset of human faces used for training StyleGAN2 ([[https://github.com/NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset)]([https://github.com/NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset)) (accessed on 30 December 2022)). Moreover, to diversify the data corpus and test generalization capabilities, we considered additional sets of real images coming from different sources, in particular:  
* _CelebA_: a subset of [[https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)]([https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)) (accessed on 30 December 2022) [29], proposed for face detection and recognition, landmark localization, and face editing. * _CelebHQ_: a subset of the [[https://mmlab.ie.cuhk.edu.hk/projects/CelebA/CelebAMask_HQ.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA/CelebAMask_HQ.html)]([https://mmlab.ie.cuhk.edu.hk/projects/CelebA/CelebAMask_HQ.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA/CelebAMask_HQ.html)) (accessed on 30 December 2022) dataset [30], proposed for evaluating algorithms in face parsing, recognition, and generation. * _Caltech_: a subset of [[https://data.caltech.edu/records/6](https://data.caltech.edu/records/6) riah-hdv18]([https://data.caltech.edu/records/6](https://data.caltech.edu/records/6) riah-hdv18) (accessed on 30 December 2022) involving 27 subjects with different expressions and under different illumination conditions. * _LFW_: a subset of the [[http://vis-www.cs.umass.edu/lfw/](http://vis-www.cs.umass.edu/lfw/)]([http://vis-www.cs.umass.edu/lfw/](http://vis-www.cs.umass.edu/lfw/)) (accessed on 30 December 2022) (LFW) dataset [27], a public benchmark for face verification. Details about the data used in the experiments are reported in Figure 6.  
For each image, we applied the inversion process and obtained its reconstructed version; we used the default parameters of the inversion algorithms and fixed the random seed for reproducibility. As a pre-processing step for all images before the inversion, we detected the squared area containing the face through the [[https://github.com/davisking/dlib](https://github.com/davisking/dlib)]([https://github.com/davisking/dlib](https://github.com/davisking/dlib)) (accessed on 30 December 2022) library, and blurred the background outside that area to retain mostly face information in the input data. If needed, we res  
Figure 5: (a) Landmarks numbered from 1 to 68; (b) Landmarks detected on a face. We can identify 5 areas: face line (red landmarks [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]), eyebrows [18; 19; 20; 21; 22; 23; 24; 25; 26; 27], and eyes [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48], both in green (we call them eye area), nose [28; 29; 30; 31; 32; 33; 34; 35; 36] in yellow, and mouth [49; 50; 51; 52; 53; 54; 55; 56; 57; 58] in blue. Figure 6: Summary of the face image data used in the experiments. [[https://pillow.readthedocs.io/en/stable/](https://pillow.readthedocs.io/en/stable/)]([https://pillow.readthedocs.io/en/stable/](https://pillow.readthedocs.io/en/stable/)) (accessed on 30 December 2022) library) to the resolution 1024 \(\times\) 1024, which is the one accepted by the inversion algorithm.