**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).