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| **Update: Edited & AI-Generated Content Detection – Project Plan** | |
| ### 🔍 Phase 1: Rule-Based Image Detection (In Progress) | |
| We're implementing three core techniques to individually flag edited or AI-generated images: | |
| * **ELA (Error Level Analysis):** Highlights inconsistencies via JPEG recompression. | |
| * **FFT (Frequency Analysis):** Uses 2D Fourier Transform to detect unnatural image frequency patterns. | |
| * **Metadata Analysis:** Parses EXIF data to catch clues like editing software tags. | |
| These give us visual + interpretable results for each image, and currently offer \~60–70% accuracy on typical AI-edited content. | |
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| ### Phase 2: AI vs Human Detection System (Coming Soon) | |
| **Goal:** Build an AI model that classifies whether content is AI- or human-made — initially focusing on **images**, and later expanding to **text**. | |
| **Data Strategy:** | |
| * Scraping large volumes of recent AI-gen images (e.g. SDXL, Gibbli, MidJourney). | |
| * Balancing with high-quality human images. | |
| **Model Plan:** | |
| * Use ELA, FFT, and metadata as feature extractors. | |
| * Feed these into a CNN or ensemble model. | |
| * Later, unify into a full web-based platform (upload → get AI/human probability). | |