Classification of Real Vs AI Generated Images Using Deep Learning

This paper presents a Classification of Real vs AI images using Deep Learning With the rise of powerful AI-generated images, spotting the difference between real and synthetic pictures has become more important than ever. Deep learning, particularly convolutional neural networks (CNNs) and transformer-based models, is helping us tackle this challenge. These models analyze tiny details in images—such as textures, patterns, and hidden artifacts—to detect whether an image was created by AI tools like GANs or diffusion models. This technology has practical uses in many areas, from preventing misinformation and verifying images in journalism to strengthening cybersecurity against deep fakes. By training on a diverse mix of real and AI-generated images, these models learn to classify them with high accuracy. Tools like Grad-CAM can even show which parts of an image influenced the model’s decision, making AI’s reasoning more transparent. Ultimately, automating the detection of AI-generated images helps protect intellectual property, maintain trust in digital media, and support law enforcement in digital forensics. Whether it’s for social media, news verification, or cybersecurity, this technology is becoming an essential tool in today’s AI-driven world. With the rapid advancements in AI, it’s becoming harder to tell whether an image is real or generated by artificial intelligence. Deep learning models, especially CNNs and transformer-based architectures, are now being used to analyze subtle details and detect synthetic images. These models learn to recognize patterns, textures, and artifacts unique to AI-generated content, making them valuable tools in digital forensics and misinformation detection. By training on a diverse dataset of real and AI-generated images, they can classify images with high accuracy. Tools: Python, Flask, PyTorch, TorchVision, Html, Css, JavaScript.