Image Classification And Explainable Identification Of AI – Generated Synthetic Images

Recent advances in synthetic image generation, particularly through artificial intelligence, have led to the creation of images so realistic that they are virtually indistinguishable from real photographs. This presents significant challenges for data authenticity and reliability, especially in areas such as journalism, social media, and scientific research, where the integrity of images is critical. This study proposes an approach to effectively distinguish between real and AI-generated images using a deep learning model based on ResNet50. The classification task is framed as a binary problem, where images are categorized as either "real" or "AI-generated." While synthetic images can replicate complex visual details such as lighting, reflections, and textures, subtle visual imperfections often differentiate them from genuine photographs. The study investigates these differences, focusing on minor artifacts and inconsistencies that are typically present in AI-generated content, such as background distortions, lighting anomalies, and unnatural textures. These artifacts are not always perceptible to the human eye, but can be reliably detected by machine learning models. The ResNet50 model is employed to learn and classify these visual cues, enabling the system to achieve high accuracy in distinguishing real images from synthetic ones. By training on a large dataset of both real and AI-generated images, the model identifies key image features that serve as indicators of authenticity. The study also explores the interpretability of the model's decisions, shedding light on which aspects of the images are most informative for classificationInspection of structural cracks is critical for maintaining the safety and longevity of bridges and other infrastructure. Traditional methods for crack detection are often manual, labor-intensive, and prone to human error. Recent advances in deep learning and semantic segmentation provide a promising alternative, but obtaining high-quality annotated data remains a significant challenge. This paper introduces an enhanced approach to crack detection using deep learning, leveraging synthetic data generation and advanced semantic segmentation techniques. We propose the use of DeepLabV3 with a ResNet50 backbone, an extension of the DeepLabV3 architecture that incorporates a robust ResNet50 feature extractor to improve segmentation. Our approach involves generating synthetic crack images to address the data scarcity issue. This is achieved using the StyleGAN3 for realistic image synthesis. By integrating these synthetic datasets with the DeepLabV3+ model, we aim to boost segmentation performance beyond the capabilities of standard models. Hyperparameter tuning is performed to optimize the DeepLabV3 with ResNet50 configuration, achieving significant improvements in segmentation. We employ data augmentation techniques such as motion blur, zoom, and defocus to further refine model performance. The proposed method is evaluated against existing state-of-the-art techniques, demonstrating superior accuracy. The results indicate that our approach not only enhances the crack detection but also offers a novel application of synthetic data generation in deep learning for semantic segmentation. This research provides new insights into leveraging advanced neural networks and synthetic data for improved structural crack analysis.