Update README.md

README.md

@@ -64,4 +64,17 @@ with torch.no_grad():
 # Map class index to label
 label = model.config.id2label[predicted_class]
 print(f"Predicted Label: {label}")
-```
+```
+# **Limitations**  
+1. **Generalization Issues** – The model may not perform well on deepfake images generated by unseen or novel deepfake techniques.  
+2. **Dataset Bias** – The training data might not cover all variations of real and fake images, leading to biased predictions.  
+3. **Resolution Constraints** – Since the model is based on `vit-base-patch16-224-in21k`, it is optimized for 224x224 image resolution, which may limit its effectiveness on high-resolution images.  
+4. **Adversarial Vulnerabilities** – The model may be susceptible to adversarial attacks designed to fool vision transformers.  
+5. **False Positives & False Negatives** – The model may occasionally misclassify real images as deepfake and vice versa, requiring human validation in critical applications.  
+
+# **Intended Use**  
+1. **Deepfake Detection** – Designed for identifying deepfake images in media, social platforms, and forensic analysis.  
+2. **Research & Development** – Useful for researchers studying deepfake detection and improving ViT-based classification models.  
+3. **Content Moderation** – Can be integrated into platforms to detect and flag manipulated images.  
+4. **Security & Forensics** – Assists in cybersecurity applications where verifying the authenticity of images is crucial.  
+5. **Educational Purposes** – Can be used in training AI practitioners and students in the field of computer vision and deepfake detection.