# Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis
## 5 Conclusions  
We have explored a forensic detection strategy for identifying synthetic face images based on the inversion of the GAN synthesis process. The experimental results demonstrate that a proper biometric comparison between the image under investigation and its reconstruction through an inversion algorithm allows for distinguishing images that have been synthesized by the considered generator and those who are not. In particular, our analysis shows that landmark-based feature representations are particularly effective for this purpose.  
A desirable aspect of such an approach is that it is not purely inductive, but is based on the very architecture of the generation methods. Moreover, the best-performing features refer to an explicit face model, and they express a biometric reconstruction dissimilarity that can be better interpreted with respect to deep representations.  
On the other hand, a limitation of this approach is that it assumes prior knowledge of the candidate generator, for which an inversion procedure needs to be devised. In particular, this work focuses on a powerful yet single generator. Extensions of this work would consider more general scenarios where the inversion-based comparison is tested against multiple latest generators, such as StyleGAN3 [34] and EG3D [35]. This would also include dealing with more comprehensive data corpora with diverse facial attributes in terms of expression, gender or age, as well as more generative models that are trained to synthesize other objects beyond faces. Moreover, an effective fusion of our approach with data-driven techniques would be a promising direction for future investigations. In addition, an open research question is to which extent inversion-based techniques can be applied to generative models operating in domains other then the visual one, such as speech [36], text [37], or raw tabular data [38]. In this respect, we expect that the main concept of the paper still remains valid, while the metrics should be suitably revised to capture the most significant domain-sensitive differences.  
Figure 16: Shared images before (top row) and after (bottom row) the reconstruction.  
**Author Contributions:** Conceptualization, C.P.; Data curation, F.L., D.L., and G.A.; Methodology, C.P., F.L., D.L., G.A., G.B., and F.D.N.; Project administration, C.P.; Software, F.L., D.L., and G.A.; Supervision, C.P., G.B., and F.D.N.; Writing--original draft, C.P., F.L., D.L., and G.A.; Writing--review and editing, G.B. and F.D.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has received funding by the project PREMIER (PREServing Media trustworthiness in the artificial Intelligence ERa) under contract PRIN 2017 2017Z595 XS-001, funded by the Italian Ministry of University and Research, from the TruBlo consortium as part of the TrueBees project ([[https://www.truble.eu/truebees/](https://www.truble.eu/truebees/)]([https://www.truble.eu/truebees/](https://www.truble.eu/truebees/)) (accessed on 30 December 2022)) funded by TruBlo under the Europe's Horizon 2020 programme (grant agreement No. 957228), and by the Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00112090136.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset of reconstructed images is available at [[https://tinyurl.com/puusfcke](https://tinyurl.com/puusfcke)]([https://tinyurl.com/puusfcke](https://tinyurl.com/puusfcke)) (accessed on 30 December 2022).  
**Conflicts of Interest:** The authors declare no conflicts of interest.