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In biomedical research and artificial intelligence, access to large,well-balanced, and representative datasets is crucial for developingtrustworthy applications that can be used in real-world scenarios. However,obtaining such datasets can be challenging, as they are often restricted tohospitals and specialized facilities. To address this issue, the study proposesto generate highly realistic synthetic faces exhibiting drug abuse traitsthrough augmentation. The proposed method, called "3DG-GA", Deep De-identifiedanonymous Dataset Generation, uses Genetics Algorithm as a strategy forsynthetic faces generation. The algorithm includes GAN artificial facegeneration, forgery detection, and face recognition. Initially, a dataset of120 images of actual facial drug abuse is used. By preserving, the drug traits,the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuseimages. The dataset will be open to the scientific community, which canreproduce our results and benefit from the generated datasets while avoidinglegal or ethical restrictions.

Generation of artificial facial drug abuse images using Deep De-identified anonymous Dataset augmentation through Genetics Algorithm (3DG-GA)