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README.md

@@ -12,13 +12,13 @@ tags:
 
 RealityGuardAI is an ultra-fast, highly optimized AI engine specifically designed to detect visual artifacts, behavioral anomalies, and audio-visual desynchronization commonly found in AI-generated Deepfake videos and images.
 
-This Model Repository specifically hosts the **Lightweight Artifact CNN** weights, which act as the core visual detection module of the larger RealityGuardAI pipeline.
+This Model Repository specifically hosts the **Lightweight Artifact CNN** weights, which act as the core deep learning visual detection module of the larger RealityGuardAI pipeline.
 
 ## 🏗️ Full Pipeline Architecture
 
 ![RealityGuardAI Architecture](architecture.jpg)
 
-This CNN operates at **Stage 7** of our massive multimodal pipeline. To view, run, and experiment with the **entire pipeline** (including Lip-Sync Detection, Blink Analysis, and FastAPI integrations), please visit the official GitHub repository:
+This CNN operates at **Stage 7.3** of our massive multimodal pipeline. To view, run, and experiment with the **entire pipeline** (including Lip-Sync Detection, Blink Analysis, Handcrafted Feature Extraction, and FastAPI integrations), please visit the official GitHub repository:
 
 👉 **[Satyam-123336/DeepFake-Detection on GitHub](https://github.com/Satyam-123336/DeepFake-Detection)**
 
@@ -30,11 +30,13 @@ This CNN operates at **Stage 7** of our massive multimodal pipeline. To view, ru
 - **Task:** Binary Classification (0 = Real, 1 = Fake).
 - **Format:** PyTorch State Dictionary (`.pt`).
 
-## 🎯 Intended Use
-This model analyzes cropped facial frames to detect:
-- Unnatural lighting asymmetry
-- Texture spread and blending inconsistencies
-- Generative AI artifacts (GAN/Diffusion traces)
+## 🎯 Intended Use & Capabilities
+This Model Repository hosts the weights specifically for the deep learning component (Stage 7.3) of RealityGuardAI. 
+
+While the full pipeline uses classical OpenCV algorithms to detect lighting asymmetry and texture spread (Stage 7.2), this PyTorch CNN model is specifically designed to analyze raw facial crops to detect:
+- **Generative AI artifacts** (GAN/Diffusion traces and upscaling noise)
+- **Deep pixel-level blending inconsistencies** (where a fake face is stitched onto a real head)
+- **Structural anomalies** generated by deepfake rendering engines
 
 ## 💻 How to Use
 You can easily integrate this CNN into your own PyTorch projects using the `huggingface_hub` library. It will automatically download the weights if they are missing locally.