Name
Abstract | ||
|---|---|---|
The Deepfake phenomenon has become very popular nowadays thanks to the possibility to create incredibly realistic images using deep learning tools, based mainly on ad-hoc Generative Adversarial Networks (GAN). In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method able to detect a forensics trace hidden in images: a sort of fingerprint left in the image generation process. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process. Ad-hoc validation has been employed through experimental tests with naive classifiers on five different architectures (GDWCT, STARGAN, ATTGAN, STYLEGAN, STYLEGAN2) against the CELEBA dataset as ground-truth for non-fakes. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPRW50498.2020.00341 | CVPR Workshops |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guarnera Luca | 1 | 1 | 0.34 |
| oliver giudice | 2 | 13 | 3.21 |
| Sebastiano Battiato | 3 | 659 | 78.73 |