LSiF: Log-Gabor Empowered Siamese Federated Learning for Efficient Obscene Image Classification in the Era of Industry 5.0

The widespread presence of explicit content on social media platforms has far-reaching consequences for individuals, relationships, and society as a whole. It is crucial to tackle this problem by implementing efficient content moderation, educating users, and creating technologies and policies that foster a more secure and wholesome online atmosphere. To address this issue, this research proposes the Log-Gabor Empowered Siamese Federated Learning (LSiF) framework for precise and efficient classification of obscene images in the era of Industry 5.0. The LSiF framework utilizes a Siamese Network with two parallel streams, where log-Gabor input and normal raw input are processed simultaneously. This Siamese architecture leverages shared weights and parameters, enabling effective learning of distinctive features for class differentiation and pattern recognition. The weight-sharing mechanism enhances the model’s ability to generalize, increases its robustness, and improves computational efficiency, making it well-suited for resource-constrained and real-time applications. Additionally, federated learning is employed with a client size of three, allowing local model updates on each device. This approach minimizes the need for extensive data transmission to a central server, reducing communication overhead and improving learning efficiency, particularly in environments with limited bandwidth. The proposed LSiF model demonstrates remarkable performance, achieving an accuracy of 94.30%, precision of 94.00%, recall of 94.26%, and F1-Score of 94.17% with a client size of three.