A Multimodal Feature Representation Model for Transfer-Learning-Based Identification of Images

Digital image classification assists in distinguishing natural and synthetic images to detect computer-generated objects. However, CGI improvements make it difficult to discern synthetic photos from genuine ones. Researchers suggest multiple deep learning strategies to differentiate these photo sets utilizing thorough feature analysis. These models are either complex or do not handle image sub-components, decreasing efficiency in large-scale applications. These models fail categorically. To address these issues, this work proposes a novel high-density bio-inspired feature analysis deep learning model for natural and synthetic image sub-classification. A YoLo model initially recognizes objects in input image sets. Processed separately, a hybrid LSTM/GRU model predicts high-density feature sets, which are processed by Elephant Herding Optimization (EHO) Models to identify high inter-class variance feature sets. A customized 1D CNN model is used to categorize the desired features into natural and synthetic components. These classification results establish whether the input image is natural, synthetic, or both. In real-time scenarios, the proposed model is able to improve standard classification models with 8.7% greater accuracy, 10.9% higher precision, 3.2% higher recall, and 8.4% higher AUC.