Deep Learning-Based Detection of Real and AI-Generated Images With Multiple Generators and Multi-Scaling Features
The pervasiveness those involving computer generated images, especially in the last decade has seen massive generational leaps, with the likes of GANs, autoencoders, and diffusion models now advanced enough to produce images so real that distinguishing between such and existing pictures becomes an uphill task. This paper serves a significant role in the improvement of modern deep learning models that can tackle different AI image generators and their performance in simulated images. The authors provide data pools with collected datasets to close previous research on gap existence such as limited cross-generator generalization, lack of focus on fine-grained relic detection, and absence of accurate alterations. The abundant dataset being discussed is a tangle of real imaginations, faked media Recognition video sequences, StyleGAN-made pictures, ProGAN/PGGAN outputs, and Stable Diffusion artificial visuals which were collected from Kaggle. Each of the dataset classes was leveled in quantity and then made rugged with different types of real-world alterations like solidity pieces, noise in low sunlit, occlusions, impression, and so on. A variety of other artifacts occurring in the output were ultra local and required more specific information for elimination. Plans were further made to create a model that would perform better with structured information based on the global context such as the frame-level status with fine-grained artifacts around and the proposed improvement focuses on the recognition of textured elements in the image on the level of individual parts. Extensive and exhaustive tests assessing the estimation features based on F1-score, precision, accuracy, recall, ROC-AUC metric as well as cross-generator estimation which involve the very behavior depreciation as a weakness revealed that the mentioned framework can be applied to new unseen generative models while preserving the proposed optimality with the diffusion-based datasets included.