Updated Interface + New Images

.gitattributes

@@ -7,6 +7,7 @@
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
 *.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,6 +1,6 @@
 ---
-title: SuSy
-emoji: 🔎
+title: SuSy Game
+emoji: 🕵️
 colorFrom: gray
 colorTo: pink
 sdk: gradio
@@ -8,7 +8,7 @@ sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: Spot AI-Generated images with SuSy!
+short_description: Compete against SuSy to spot AI-Generated images!
 ---
 
 # SuSy - Synthetic Image Detector

app.py

@@ -7,8 +7,8 @@ from skimage.feature import graycomatrix, graycoprops
 from torchvision import transforms
 import os
 
-NUM_ROUNDS = 5  # Adjust the number of game rounds here
-PROB_THRESHOLD = 0.5  # Adjust the probability threshold for model prediction here
+NUM_ROUNDS = 10  # Adjust the number of game rounds here
+PROB_THRESHOLD = 0.3  # Adjust the probability threshold for model prediction here
 
 # Load the model
 model = torch.jit.load("SuSy.pt")
@@ -72,7 +72,7 @@ class GameState:
         self.user_score = 0
         self.model_score = 0
         self.current_round = 0
-        self.total_rounds = 2
+        self.total_rounds = NUM_ROUNDS
         self.game_images = []
         self.is_game_active = False
         self.last_results = None
@@ -88,7 +88,7 @@ def load_images():
     fake_image_folder = "fake_images"
     real_images = [os.path.join(real_image_folder, img) for img in os.listdir(real_image_folder)]
     fake_images = [os.path.join(fake_image_folder, img) for img in os.listdir(fake_image_folder)]
-    selected_images = random.sample(real_images, 1) + random.sample(fake_images, 1)
+    selected_images = random.sample(real_images, NUM_ROUNDS / 2) + random.sample(fake_images, NUM_ROUNDS / 2)
     random.shuffle(selected_images)
     return selected_images
 
@@ -151,7 +151,7 @@ def submit_guess(user_guess):
     correct_answer = "Real" if "real_images" in game_state.game_images[game_state.current_round] else "Fake"
     
     # Determine model's guess based on probabilities
-    model_guess = "Real" if model_prediction['Authentic'] > 0.5 else "Fake"
+    model_guess = "Real" if model_prediction['Authentic'] > PROB_THRESHOLD else "Fake"
     
     # Update scores
     if user_guess == correct_answer:
@@ -208,8 +208,25 @@ custom_css = """
 # Define Gradio interface
 with gr.Blocks(css=custom_css) as iface:
     with gr.Column(elem_id="game-container"):
-        gr.Markdown("# Real or Fake Image Challenge")
-        gr.Markdown("Can you beat the AI at detecting synthetic images?")
+        gr.HTML("""
+            <table style="border-collapse: collapse; border: none; padding: 20px;">
+            <tr style="border: none;">
+                <td style="border: none; vertical-align: top; padding-right: 30px; padding-left: 30px;">
+                <img src="https://cdn-uploads.huggingface.co/production/uploads/62f7a16192950415b637e201/NobqlpFbFkTyBi1LsT9JE.png" alt="SuSy Logo" width="120" style="margin-bottom: 10px;">
+                </td>
+                <td style="border: none; vertical-align: top; padding: 10px;">
+                <p style="margin-bottom: 15px;">Compete against SuSy to spot AI-Generated images! SuSy can distinguish between authentic images and those generated by DALL·E, Midjourney and Stable Diffusion.</p>
+                <p style="margin-top: 15px;">Learn more about SuSy: <a href="https://arxiv.org/abs/2409.14128">Present and Future Generalization of Synthetic Image Detectors</a></p>
+                <p style="margin-top: 15px;">
+                    Enter the SuSy-verse!
+                    <a href="https://huggingface.co/HPAI-BSC/SuSy">Model</a> |
+                    <a href="https://github.com/HPAI-BSC/SuSy">Code</a> |
+                    <a href="https://huggingface.co/datasets/HPAI-BSC/SuSy-Dataset">Dataset</a>
+                </p>
+                </td>
+            </tr>
+            </table>
+        """)
         
         with gr.Row():
             with gr.Column(scale=2):