CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images

AI-generated images are now visually indistinguishable from real photographs, making it increasingly difficult for humans to detect synthetic content without technological assistance—a critical concern for data reliability and authentication.

Computer vision models can effectively detect AI-generated images with high accuracy, with the study achieving 92.98% accuracy in classifying images as real or AI-generated using a Convolutional Neural Network trained on synthetic and real image datasets.

Detection relies on subtle background artifacts rather than the main subject, as explainable AI analysis revealed that the model identifies AI-generated images through anomalies in peripheral features and textures rather than the primary object itself—suggesting AI generation leaves detectable traces in fine details.

Synthetic image generation technology has advanced significantly, with latent diffusion models now capable of creating complex visual attributes like photorealistic water reflections, demonstrating the sophistication of current AI image generation tools that pose challenges for authentication.

Explainable AI methods are essential for understanding detection mechanisms, as the study used Gradient Class Activation Mapping to interpret which image features the detection model actually uses, providing transparency into how these authentication systems work.