update

app.py

@@ -101,14 +101,11 @@ demo = gr.Blocks(title="Deepfake Detection", theme=gr.themes.Soft())
 with demo:
     gr.Markdown("# 🔍 Deepfake Detection Library")
     gr.Markdown("""
-    Upload an image and select a detector to check if it's real or fake.
+    This library collects a series of state-of-the-art methods for deepfake detection, allowing for free and unlimited use.
     
-    **Available Detectors:**
-    - **R50_TF**: ResNet-50 based detector
-    - **R50_nodown**: ResNet-50 without downsampling
-    - **CLIP-D**: CLIP-based detector
-    - **P2G**: Prompt2Guard detector
-    - **NPR**: Neural Posterior Regularization
+    ### 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).
     """)
     
     with gr.Row():
@@ -130,13 +127,42 @@ with demo:
                 show_copy_button=True
             )
     
+    with gr.Accordion("📚 Model Details", open=False):
+        gr.Markdown("""
+        ### **R50_TF**
+        *   **Description**: A ResNet50 architecture modified to exclude downsampling at the first layer. It uses "learned prototypes" in the classification head for robust detection.
+        *   **Paper**: [TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks](https://arxiv.org/pdf/2504.20658)
+        *   **Code**: [GitHub Repository](https://github.com/MMLab-unitn/TrueFake-IJCNN25)
+
+        ### **R50_nodown**
+        *   **Description**: A ResNet-50 model without downsampling operations in the first layer, designed to preserve high-frequency artifacts common in synthetic images.
+        *   **Paper**: [On the detection of synthetic images generated by diffusion models](https://arxiv.org/abs/2211.00680)
+        *   **Code**: [GitHub Repository](https://grip-unina.github.io/DMimageDetection/)
+
+        ### **CLIP-D**
+        *   **Description**: A lightweight detection strategy based on CLIP features. It exhibits surprising generalization ability using only a handful of example images.
+        *   **Paper**: [Raising the Bar of AI-generated Image Detection with CLIP](https://arxiv.org/abs/2312.00195v2)
+        *   **Code**: [GitHub Repository](https://grip-unina.github.io/ClipBased-SyntheticImageDetection/)
+
+        ### **P2G (Prompt2Guard)**
+        *   **Description**: Uses Vision-Language Models (VLMs) with conditioned prompt-optimization for continual deepfake detection. It leverages read-only prompts for efficiency.
+        *   **Paper**: [Conditioned Prompt-Optimization for Continual Deepfake Detection](https://arxiv.org/abs/2407.21554)
+        *   **Code**: [GitHub Repository](https://github.com/laitifranz/Prompt2Guard)
+
+        ### **NPR**
+        *   **Description**: Focuses on Neighboring Pixel Relationships (NPR) to capture generalized structural artifacts stemming from up-sampling operations in generative networks.
+        *   **Paper**: [Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection](https://arxiv.org/abs/2312.10461)
+        *   **Code**: [GitHub Repository](https://github.com/chuangchuangtan/NPR-DeepfakeDetection)
+        """)
+
     gr.Markdown("""
     ---
-    ### About
-    This Space provides access to multiple state-of-the-art deepfake detection models. 
-    All models are trained on StyleGAN2, StableDiffusionXL, FFHQ, and FORLAB datasets.
-    
-    **Note:** First detection may be slower due to model loading.
+    ### References
+    1. Dell'Anna, S., Montibeller, A., & Boato, G. (2025). *TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks*. arXiv preprint arXiv:2504.20658.
+    2. Corvi, R., et al. (2023). *On the detection of synthetic images generated by diffusion models*. ICASSP.
+    3. Cozzolino, D., et al. (2023). *Raising the Bar of AI-generated Image Detection with CLIP*. CVPRW.
+    4. Laiti, F., et al. (2024). *Conditioned Prompt-Optimization for Continual Deepfake Detection*. arXiv preprint arXiv:2407.21554.
+    5. Tan, C., et al. (2024). *Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection*. CVPR.
     """)
     
     submit_btn.click(