update ALL

app.py

@@ -163,10 +163,10 @@ def predict(image_path, detector_name):
             
             votes_real = 0.0
             votes_fake = 0.0
+            total_weight_used = 0.0
             confidences = []
             labels = []
             colors = []
-            total_conf = 0
             
             for det, res in results:
                 pred = res.get('prediction', 'Unknown')
@@ -183,16 +183,19 @@ def predict(image_path, detector_name):
                 labels.append(det)
                 confidences.append(score)
                 colors.append(color)
-                total_conf += score
                 
                 # 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})
                     if pred == 'fake':
-                        votes_fake += weights['fake']
+                        w = weights['fake']
+                        votes_fake += w * score
+                        total_weight_used += w
                     elif pred == 'real':
-                        votes_real += weights['real']
+                        w = weights['real']
+                        votes_real += w * score
+                        total_weight_used += w
             
             # Majority Voting
             if votes_real > votes_fake:
@@ -202,15 +205,19 @@ def predict(image_path, detector_name):
             else:
                 verdict = "UNCERTAIN"
             
-            avg_conf = total_conf / len(results) if results else 0
+            # Calculate weighted average confidence
+            if total_weight_used > 0:
+                weighted_conf = (votes_real + votes_fake) / total_weight_used
+            else:
+                weighted_conf = 0.0
             
             # Explanation
             if verdict == "REAL":
-                explanation = f"Considering the results obtained by all models (weighted by their historical performance), the analyzed image results, with an average confidence of {avg_conf:.4f}, not produced by a generative AI."
+                explanation = f"Considering the results obtained by all models (weighted by their historical performance), the analyzed image results, with a weighted confidence of {weighted_conf:.4f}, not produced by a generative AI."
             elif verdict == "FAKE":
-                explanation = f"Considering the results obtained by all models (weighted by their historical performance), the analyzed image results, with an average confidence of {avg_conf:.4f}, produced by a generative AI."
+                explanation = f"Considering the results obtained by all models (weighted by their historical performance), the analyzed image results, with a weighted confidence of {weighted_conf:.4f}, produced by a generative AI."
             else:
-                explanation = f"The result is uncertain. The detectors produced unconsistent results. The average confidence is {avg_conf:.4f}."
+                explanation = f"The result is uncertain. The detectors produced unconsistent results. The weighted confidence is {weighted_conf:.4f}."
             
             # Plotting
             fig, ax = plt.subplots(figsize=(10, 5))
@@ -295,7 +302,8 @@ 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. Only confident predictions (> 0.6) are counted.
+    *   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.
+    *   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.
 
@@ -325,6 +333,20 @@ with demo:
             )
             plot_output = gr.Plot(label="Confidence Scores")
     
+    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).
+        
+        | 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} |
+        """)
+
     with gr.Accordion("📚 Model Details", open=False):
         gr.Markdown("""
         ### **ALL**

verify_all_option.py

@@ -41,10 +41,10 @@ def test_all_option():
             
             if isinstance(fig, plt.Figure):
                 print("Figure created successfully.")
-                if "weighted by their historical performance" in text:
-                 print("Text output confirms weighted voting is used.")
+                if "weighted confidence" in text:
+                 print("Text output confirms weighted confidence is reported.")
             else:
-                 print("Text output MISSING weighted voting confirmation.")
+                 print("Text output MISSING weighted confidence confirmation.")
 
             expected_verdict = "produced by a generative AI" # Majority Fake
             if expected_verdict in text: