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Analyzes uploaded images using a trained neural network classifier to distinguish between human-created photographs and synthetically generated images (likely from diffusion models or GANs). The system processes visual features like artifact patterns, texture consistency, and statistical anomalies that are characteristic of generative AI outputs, returning a binary or confidence-scored classification result.

Wraps the detection capability in a game interface where users submit images and receive immediate feedback on whether their guess (human or AI) matches the classifier's prediction. The system tracks user performance metrics and may use aggregated user guesses as training signal or validation data, creating a feedback loop that improves user intuition over repeated rounds.

Allows users to submit multiple images in sequence (or potentially batch) and tracks cumulative performance metrics across the session, including accuracy rate, speed of classification, and possibly comparison against baseline human performance or other users. The backend likely maintains session state and aggregates statistics for display.

The underlying classifier is trained or fine-tuned to recognize artifacts and patterns from multiple generative AI architectures (diffusion models like Stable Diffusion/DALL-E, GANs, potentially autoregressive models). The system likely uses ensemble methods or a single large model trained on diverse synthetic image datasets to generalize across generation techniques rather than being tuned to a single model's output.