This paper introduces a novel methodology for diagnosing COVID-19 leveraging Generative Adversarial Networks for Conditional Tabular data (GANCT). GANCT constructs a sophisticated conditional generative adversarial framework specialized for precise modeling of statistical distributions endemic to COVID-19 tabular datasets. Extensive experiments quantify the impact on predictive performance from augmenting the original data with synthetic GANCT samples. Results prove superior COVID-19 screening accuracy and ROC AUC across classifiers when supplementing real data with GANCT synthesizations. Moreover, we identified the optimal augmentation range that markedly improves performance while avoiding overfitting. The key contributions are: 1) A customized GAN architecture for COVID-19 tabular data synthesis, 2) Comprehensive evaluations demonstrating the benefits of GANCT augmentation, 3) Identification of optimal augmentation levels, and 4) Substantial improvements in COVID-19 prediction. GANCT can help overcome data scarcity to develop more reliable AI diagnostic systems for the pandemic.

In order to contribute to safe sharing of 3D mul-tiresolution meshes, a new approach of crypto-watermarking is proposed. This approach is based on the use of RSA algorithm and spherical harmonics transform. The aim being is to increase the integration rate while maintaining mesh quality on the one hand and extract correctly inserted image on the other hand. To embed data, host mesh is decomposed using spherical harmonics transform. Resulting coefficients are watermarked twice to insert the grayscale image already encrypted using RSA algorithm. Finally, watermarked mesh is reconstructed through the use on 10% of coefficients already calculated. Found results prove that our approach is able to insert a high amount of data without influencing the mesh quality. The application of the most popular attacks does not prevent a correct extraction of data already inserted. Our algorithm is,then,robust against these attacks.