update README

README.md

@@ -12,21 +12,22 @@ license: mit
 
 # Deepfake Detection Space (Open-Source Demonstrator)
 
-Welcome to the Truebees Deepfake Detection Space! This Space provides a unified, user-friendly interface to test multiple open-source state-of-the-art deepfake detection models on your own images. 
+Welcome to the Truebees Deepfake Detection Space! This Space provides an **educational, open-source demonstrator** collecting a series of baseline state-of-the-art methods for deepfake detection, allowing for free and unlimited use. 
 
-These detectors are available in the [Image-Deepfake-Detectors-Public-Library](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
+If you want to train the detectors in this Space yourself, check out our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
 
 ## 🎯 Goal and Context
 
-This demonstrator is designed as an **educational and open-source contribution** to the community. We want to allow users, researchers, and developers to experiment with baseline forensic models for free, familiarize themselves with AI detection tools, and understand both their capabilities and their limitations.
+This demonstrator is designed as an open-source contribution to the community. We want to allow users, researchers, and developers to experiment with baseline forensic models for free, familiarize themselves with AI detection tools, and understand both their capabilities and their limitations.
 
-**⚠️ Important Limitations & Disclaimer:**
-* **Non-Social Training Data:** The models included here were trained specifically on non-social datasets. 
-* **"In-the-Wild" Performance Drop:** Because these models lack training on highly compressed data, you may experience a severe drop in performance or inaccurate results when testing images downloaded from social media platforms or messaging apps (e.g., WhatsApp). 
-* **Not Proprietary Software:** This Space showcases open-source baseline networks. It **does not** reflect the performance, robustness, or accuracy of Truebees' proprietary commercial detection methods, which are designed to handle complex "in-the-wild" manipulations.
+**⚠️ Important:**
+* **Not Proprietary Software:** This tool showcases open-source detectors. It **does not** reflect the performance, robustness, or accuracy of Truebees' commercial detection methods. Want to give it a try? check **[Truebees Website](https://www.truebees.eu)**.
+* **Non-Social Training Data:** These models were trained on images not shared on social networks from the **[DeepShield dataset](https://zenodo.org/records/15648378)**. You may experience a drop in accuracy when testing highly compressed images downloaded from social media platforms or messaging apps (e.g., WhatsApp). 
+* **Expected Performance:** You can expect performance comparable to the results shown in [Dell'Anna et al. (2025)](https://arxiv.org/pdf/2504.20658).
 
 ## 🛠️ Available Detectors
 
+* **ALL**: Runs all available detectors sequentially. Produces a Weighted Majority Vote verdict based on confident predictions (> 0.6) and generates a Confidence Plot.
 * **R50_TF**: ResNet-50 based detector trained on the TrueFake dataset.
 * **R50_nodown**: ResNet-50 without downsampling operations.
 * **CLIP-D**: CLIP-based deepfake detector.
@@ -36,23 +37,30 @@ This demonstrator is designed as an **educational and open-source contribution**
 ## 🚀 Usage Guide
 
 1. **Upload an image**: Choose an image you want to analyze.
-2. **Select a detector**: Choose one of the open-source models from the dropdown menu.
+2. **Select a detector**: Choose one of the open-source models (or "ALL") from the dropdown menu.
 3. **Detect**: Click the "Detect" button to generate the prediction.
 
-**Understanding the Output:**
-* **Prediction**: Indicates whether the model believes the image is "Real" or "Fake".
-* **Confidence**: The model's confidence score, ranging from 0 to 1.
-* **Elapsed Time**: Total processing time taken by the model.
+**Understanding the Results:**
+* **Prediction**: Tells if an image is **Real** or **Fake**.
+* **Confidence**: The confidence with which the model determines if the image is real or fake.
+* **Elapsed Time**: The time the model needed to make the prediction (excluding preprocessing or model building).
+
+**Understanding the Results produced by "ALL":**
+* Produces a **Weighted Majority Vote** verdict (Real/Fake). 
+* Each model's vote is weighted by a fixed importance score based on empirical results from the **DeepShield dataset**, following this ranking: **CLIP-D > R50_TF > R50_nodown > P2G > NPR**. The weights are scaled so that their sum equals 1. Only confident predictions (> 0.6) are counted.
+* 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.
 
 ## 🧠 Training Data Overview
 
-All open-source models featured in this Space have been pretrained on specific datasets:
-* **Fake Images:** Generated using StyleGAN2 and StableDiffusionXL.
+All open-source models featured in this Space have been pretrained using the **[DeepShield dataset](https://zenodo.org/records/15648378)**:
+* **Fake Images:** Generated using StyleGAN 2 and Stable Diffusion XL.
 * **Real Images:** Sourced from the FFHQ Dataset and the FORLAB Dataset.
 
 ## 📚 References
 
-For technical details, implementation specifics, and benchmarking data, please visit our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
+For technical details, full code to train and test these models on your own dataset, and benchmarking data, please visit our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
+
+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.
 
 ## 🤝 Acknowledgment
 
@@ -61,4 +69,5 @@ This work has been funded by the European Union under Horizon Europe Grant Agree
 
 ## ⏳ Note
 
-⚠️ Due to file size limitations, model weights need to be downloaded automatically upon first use. This may take a few moments.
+* ⚠️ Due to file size limitations, model weights need to be downloaded automatically on first use. This may take a few moments.
+* ⚠️ To provide a free service, all models run on CPU. The detection process may take a few seconds, depending on the image size and the selected detector.

README_HF.md

@@ -12,21 +12,22 @@ license: mit
 
 # Deepfake Detection Space (Open-Source Demonstrator)
 
-Welcome to the Truebees Deepfake Detection Space! This Space provides a unified, user-friendly interface to test multiple open-source state-of-the-art deepfake detection models on your own images. 
+Welcome to the Truebees Deepfake Detection Space! This Space provides an **educational, open-source demonstrator** collecting a series of baseline state-of-the-art methods for deepfake detection, allowing for free and unlimited use. 
 
-These detectors are available in the [Image-Deepfake-Detectors-Public-Library](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
+If you want to train the detectors in this Space yourself, check out our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
 
 ## 🎯 Goal and Context
 
-This demonstrator is designed as an **educational and open-source contribution** to the community. We want to allow users, researchers, and developers to experiment with baseline forensic models for free, familiarize themselves with AI detection tools, and understand both their capabilities and their limitations.
+This demonstrator is designed as an open-source contribution to the community. We want to allow users, researchers, and developers to experiment with baseline forensic models for free, familiarize themselves with AI detection tools, and understand both their capabilities and their limitations.
 
-**⚠️ Important Limitations & Disclaimer:**
-* **Non-Social Training Data:** The models included here were trained specifically on non-social datasets. 
-* **"In-the-Wild" Performance Drop:** Because these models lack training on highly compressed data, you may experience a severe drop in performance or inaccurate results when testing images downloaded from social media platforms or messaging apps (e.g., WhatsApp). 
-* **Not Proprietary Software:** This Space showcases open-source baseline networks. It **does not** reflect the performance, robustness, or accuracy of Truebees' proprietary commercial detection methods, which are designed to handle complex "in-the-wild" manipulations.
+**⚠️ Important:**
+* **Not Proprietary Software:** This tool showcases open-source detectors. It **does not** reflect the performance, robustness, or accuracy of Truebees' commercial detection methods. Want to give it a try? check **[Truebees Website](https://www.truebees.eu)**.
+* **Non-Social Training Data:** These models were trained on images not shared on social networks from the **[DeepShield dataset](https://zenodo.org/records/15648378)**. You may experience a drop in accuracy when testing highly compressed images downloaded from social media platforms or messaging apps (e.g., WhatsApp). 
+* **Expected Performance:** You can expect performance comparable to the results shown in [Dell'Anna et al. (2025)](https://arxiv.org/pdf/2504.20658).
 
 ## 🛠️ Available Detectors
 
+* **ALL**: Runs all available detectors sequentially. Produces a Weighted Majority Vote verdict based on confident predictions (> 0.6) and generates a Confidence Plot.
 * **R50_TF**: ResNet-50 based detector trained on the TrueFake dataset.
 * **R50_nodown**: ResNet-50 without downsampling operations.
 * **CLIP-D**: CLIP-based deepfake detector.
@@ -36,23 +37,30 @@ This demonstrator is designed as an **educational and open-source contribution**
 ## 🚀 Usage Guide
 
 1. **Upload an image**: Choose an image you want to analyze.
-2. **Select a detector**: Choose one of the open-source models from the dropdown menu.
+2. **Select a detector**: Choose one of the open-source models (or "ALL") from the dropdown menu.
 3. **Detect**: Click the "Detect" button to generate the prediction.
 
-**Understanding the Output:**
-* **Prediction**: Indicates whether the model believes the image is "Real" or "Fake".
-* **Confidence**: The model's confidence score, ranging from 0 to 1.
-* **Elapsed Time**: Total processing time taken by the model.
+**Understanding the Results:**
+* **Prediction**: Tells if an image is **Real** or **Fake**.
+* **Confidence**: The confidence with which the model determines if the image is real or fake.
+* **Elapsed Time**: The time the model needed to make the prediction (excluding preprocessing or model building).
+
+**Understanding the Results produced by "ALL":**
+* Produces a **Weighted Majority Vote** verdict (Real/Fake). 
+* Each model's vote is weighted by a fixed importance score based on empirical results from the **DeepShield dataset**, following this ranking: **CLIP-D > R50_TF > R50_nodown > P2G > NPR**. The weights are scaled so that their sum equals 1. Only confident predictions (> 0.6) are counted.
+* 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.
 
 ## 🧠 Training Data Overview
 
-All open-source models featured in this Space have been pretrained on specific datasets:
-* **Fake Images:** Generated using StyleGAN2 and StableDiffusionXL.
+All open-source models featured in this Space have been pretrained using the **[DeepShield dataset](https://zenodo.org/records/15648378)**:
+* **Fake Images:** Generated using StyleGAN 2 and Stable Diffusion XL.
 * **Real Images:** Sourced from the FFHQ Dataset and the FORLAB Dataset.
 
 ## 📚 References
 
-For technical details, implementation specifics, and benchmarking data, please visit our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
+For technical details, full code to train and test these models on your own dataset, and benchmarking data, please visit our [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
+
+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.
 
 ## 🤝 Acknowledgment
 
@@ -61,4 +69,5 @@ This work has been funded by the European Union under Horizon Europe Grant Agree
 
 ## ⏳ Note
 
-⚠️ Due to file size limitations, model weights need to be downloaded automatically upon first use. This may take a few moments.
+* ⚠️ Due to file size limitations, model weights need to be downloaded automatically on first use. This may take a few moments.
+* ⚠️ To provide a free service, all models run on CPU. The detection process may take a few seconds, depending on the image size and the selected detector.