update

README.md

@@ -1,5 +1,5 @@
 ---
-title: Deepfake Detection Library
+title: Deepfake Detection Space
 emoji: 🔍
 colorFrom: red
 colorTo: yellow
@@ -10,7 +10,7 @@ pinned: false
 license: mit
 ---
 
-# Deepfake Detection Library
+# Deepfake Detection Space
 
 This Space provides a unified interface to test multiple state-of-the-art deepfake detection models on your images.
 

app.py

@@ -75,7 +75,7 @@ def predict(image_path, detector_name):
             
             output = {
                 "Prediction": prediction,
-                "Confidence": f"{confidence:.4f}",
+                "Confidence Fake": f"{confidence:.4f}",
                 "Elapsed Time": f"{elapsed_time:.3f}s"
             }
             return json.dumps(output, indent=2)
@@ -96,16 +96,21 @@ def predict(image_path, detector_name):
 
 # Create Gradio Interface
 # Use theme only if gradio version supports it
-demo = gr.Blocks(title="Deepfake Detection", theme=gr.themes.Soft())
+demo = gr.Blocks(title="Deepfake Detection Space", theme=gr.themes.Soft())
 
 with demo:
-    gr.Markdown("# 🔍 Deepfake Detection Library")
+    gr.Markdown("# 🔍 Deepfake Detection Space")
     gr.Markdown("""
-    This library collects a series of state-of-the-art methods for deepfake detection, allowing for free and unlimited use.
+    This space collects a series of state-of-the-art methods for deepfake detection, allowing for free and unlimited use.
     
     ### Training & Performance
     All methods have been trained using the **[DeepShield dataset](https://zenodo.org/records/15648378)**, which includes images generated with **Stable Diffusion XL** and **StyleGAN 2**. 
     You can expect performance comparable to the results shown in [Dell'Anna et al. (2025)](https://arxiv.org/pdf/2504.20658).
+
+    ### Understanding the Results
+    *   **Prediction**: Tells if an image is **Real** or **Fake**.
+    *   **Confidence Fake**: The confidence with which the model determines if the image is fake.
+    *   **Elapsed Time**: The time the model needed to make the prediction (excluding preprocessing or model building).
     """)
     
     with gr.Row():