An Image Authenticity Verification System for AI-Generated Content

Recent progress in generative AI has made it possible to produce synthetic images that appear almost indistinguishable from real photographs captured by humans.Although these developments support creativity and automation, they also raise serious concerns related to misinformation, digital forgery, privacy breaches, and trustworthiness of online media.As a result, accurately identifying whether an image is generated by AI or captured by a human has become essential for digital forensics and media authenticity verification.In this project, we study the visual, statistical, and structural characteristics that differentiate real human-captured images from those generated by modern AI techniques, including GANs and diffusion-based models.In this project, we design and implement a classification system that uses machine learning and deep learning methods to identify AI-generated images.The methodology followed in this project involves collecting image data from multiple sources, performing necessary preprocessing, extracting relevant features, and training classification models based on CNN and transformer architectures.The results obtained demonstrate that the system is capable of distinguishing between real and AI-generated images with a high level of accuracy.The study also discusses implementation challenges, limitations of the current model, and the importance of updating the system as image generation technologies continue to advance.Overall, the proposed approach supports media authenticity verification, digital security, and forensic analysis.