update

app.py

@@ -34,29 +34,12 @@ if os.environ.get("SPACE_ID"):
 # Available detectors based on launcher.py
 DETECTORS = ['ALL', 'R50_TF', 'R50_nodown', 'CLIP-D', 'P2G', 'NPR']
 
-# Performance weights from Table III (Non-shared) and Table IV (Social Media: FB, TL, X)
-# Weights are averaged between Non-shared and Social Media performance.
-# R50_TF (R50-E2P): 
-#   Real (TNR): (0.98 + 0.91) / 2 = 0.95
-#   Fake (TPR): (0.85 + 0.65) / 2 = 0.75
-# R50_nodown (R50-ND): 
-#   Real (TNR): (1.00 + 0.97) / 2 = 0.98
-#   Fake (TPR): (0.79 + 0.76) / 2 = 0.77
-# CLIP-D: 
-#   Real (TNR): (0.97 + 0.94) / 2 = 0.95
-#   Fake (TPR): (0.93 + 0.91) / 2 = 0.92
-# P2G: 
-#   Real (TNR): (1.00 + 1.00) / 2 = 1.00
-#   Fake (TPR): (0.91 + 0.56) / 2 = 0.74
-# NPR: 
-#   Real (TNR): (0.99 + 1.00) / 2 = 1.00
-#   Fake (TPR): (0.60 + 0.12) / 2 = 0.36
 DETECTOR_WEIGHTS = {
-    'R50_TF': {'real': 0.95, 'fake': 0.75},
-    'R50_nodown': {'real': 0.98, 'fake': 0.77},
-    'CLIP-D': {'real': 0.95, 'fake': 0.92},
-    'P2G': {'real': 1.00, 'fake': 0.74},
-    'NPR': {'real': 1.00, 'fake': 0.36}
+    'CLIP-D': 0.30,
+    'R50_TF': 0.25,
+    'R50_nodown': 0.20,
+    'P2G': 0.15,
+    'NPR': 0.10
 }
 
 def process_image(image_path):
@@ -187,15 +170,13 @@ def predict(image_path, detector_name):
                 # Weighted Voting logic
                 # Only count vote if confidence > 0.6
                 if score > 0.6:
-                    weights = DETECTOR_WEIGHTS.get(det, {'real': 1.0, 'fake': 1.0})
+                    weight = DETECTOR_WEIGHTS.get(det, 0.0)
                     if pred == 'fake':
-                        w = weights['fake']
-                        votes_fake += w * score
-                        total_weight_used += w
+                        votes_fake += weight * score
+                        total_weight_used += weight
                     elif pred == 'real':
-                        w = weights['real']
-                        votes_real += w * score
-                        total_weight_used += w
+                        votes_real += weight * score
+                        total_weight_used += weight
             
             # Majority Voting
             if votes_real > votes_fake:
@@ -302,7 +283,7 @@ with demo:
 
     ### Understanding the Results produced by "ALL"
     *   Runs all available detectors (R50_TF, R50_nodown, CLIP-D, P2G, NPR) sequentially on the input image.
-    *   Produces a **Weighted Majority Vote** verdict (Real/Fake). Each model's vote is weighted by its historical performance (TPR for Fakes, TNR for Reals) on benchmark datasets **and its confidence score**. Only confident predictions (> 0.6) are counted.
+    *   Produces a **Weighted Majority Vote** verdict (Real/Fake). Each model's vote is weighted by a fixed importance score (summing to 1) based on user ranking **and its confidence score**. Only confident predictions (> 0.6) are counted.
     *   You can find the specific weights used for each model in the **"⚖️ Weight Details"** menu below.
     *   Also generates a **Confidence Plot** visualizing each model's score and a textual **Explanation** of the consensus.
     *   In the plot, **Green** bars indicate a **Real** prediction, while **Red** bars indicate a **Fake** prediction.
@@ -336,15 +317,15 @@ with demo:
     with gr.Accordion("⚖️ Weight Details", open=False):
         gr.Markdown(f"""
         ### **Detector Weights**
-        These weights are derived from performance on benchmark datasets (FFHQ, FORLAB, SG2, SDXL) and social media data (FB, TL, X).
+        The weights are assigned based on the ranking (based on the results of [TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks](https://arxiv.org/pdf/2504.20658)): **CLIP-D > R50_TF > R50_nodown > P2G > NPR**, such that their sum equals 1.
         
-        | Detector | Real (TNR) Weight | Fake (TPR) Weight |
-        | :--- | :---: | :---: |
-        | **R50_TF** | {DETECTOR_WEIGHTS['R50_TF']['real']:.2f} | {DETECTOR_WEIGHTS['R50_TF']['fake']:.2f} |
-        | **R50_nodown** | {DETECTOR_WEIGHTS['R50_nodown']['real']:.2f} | {DETECTOR_WEIGHTS['R50_nodown']['fake']:.2f} |
-        | **CLIP-D** | {DETECTOR_WEIGHTS['CLIP-D']['real']:.2f} | {DETECTOR_WEIGHTS['CLIP-D']['fake']:.2f} |
-        | **P2G** | {DETECTOR_WEIGHTS['P2G']['real']:.2f} | {DETECTOR_WEIGHTS['P2G']['fake']:.2f} |
-        | **NPR** | {DETECTOR_WEIGHTS['NPR']['real']:.2f} | {DETECTOR_WEIGHTS['NPR']['fake']:.2f} |
+        | Detector | Weight |
+        | :--- | :---: |
+        | **CLIP-D** | {DETECTOR_WEIGHTS['CLIP-D']:.2f} |
+        | **R50_TF** | {DETECTOR_WEIGHTS['R50_TF']:.2f} |
+        | **R50_nodown** | {DETECTOR_WEIGHTS['R50_nodown']:.2f} |
+        | **P2G** | {DETECTOR_WEIGHTS['P2G']:.2f} |
+        | **NPR** | {DETECTOR_WEIGHTS['NPR']:.2f} |
         """)
 
     with gr.Accordion("📚 Model Details", open=False):