test.md

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).