In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called “3DG-GA,” Deep De-identified Anonymous Dataset Generation, uses a Genetic Algorithm as a strategy for synthetic face generation. The algorithm includes GAN-based artificial face generation, forgery detection, and face recognition. Initially, a dataset of 120 images of actual facial drug abuse is used. By preserving the drug traits, the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse images. The dataset will be open to the scientific community, allowing others to reproduce our results and benefit from the generated datasets while avoiding legal or ethical restrictions. Additionally, we validated the dataset by training a CNN model on the synthetic images and validating it on previously unseen real images. The model achieved an accuracy of 97.2% on the unseen real images, demonstrating the high quality and applicability of the synthetic data.
The Facial Drug Abuse Synthetic Dataset, generated with Face Fusion and Genetic Algorithms, is available at:
🔗 Link to dataset: https://figshare.com/s/e03c66a0f5a3eeb5bfe8
If you find our work useful, please cite it as follows:
@article{zein2025generation,
author = {H. Zein and L. Laurent and R. Fournier and others},
title = {Generation of artificial facial drug abuse images using deep de-identified anonymous dataset augmentation through genetics algorithm (3DG-GA)},
journal = {Multimedia Tools and Applications},
year = {2025},
doi = {10.1007/s11042-025-20639-y}
}