update

README.md

@@ -1,272 +1,46 @@
-# Image Deepfake Detectors Public Library
-
-## Overview
-
-This repository provides a unified framework for training, testing, and benchmarking multiple state-of-the-art (SoA) deepfake detection models. It supports automated benchmarking, training, demo runs, and single-image detection, with modular configuration and extensible detector support.
-
-### Main Features
-
--   **Multiple Detectors:** Supports CLIP-D [1], NPR [2], P2G [3], R50_TF [4], and R50_nodown [5] (an overview for each method is provided in its `README.md`: `./detectors/<DETECTOR>/README.md`)
--   **Pretrained Weights:** All models have been pretrained on images generated with StyleGAN2 and StableDiffusionXL, and real images from the FFHQ Dataset [6] and the FORLAB Dataset [7].
--   **Automated Training & Testing:** Use `launcher.py` to run experiments across detectors and datasets.
--   **Demo Mode:** Easily test all detectors on sample images in `demo_images/`.
--   **Single Image Detection:** Run detection on individual images via the command line.
--   **Flexible Configuration:** All experiment parameters are set via YAML files in `configs/`.
--   **Logging & Results:** Logs and results are saved per detector and scenario for easy analysis.
-
 ---
-
-## Set-Up
-
-### Prerequisites
-
-`Ubuntu>=22.04.3`, `Python>=3.10` and `CUDA:12.0`
-
+title: Deepfake Detection Library
+emoji: 🔍
+colorFrom: red
+colorTo: orange
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
 ---
 
-### Download Weights
-
-You can download the weights for each model from this [link](https://drive.google.com/file/d/1F60FN2B9skRcb3YrZwhFTZQihbj3ipJQ/view?usp=sharing).
-
-Then, copy them into the `pretrained` folder for the corresponding model, following this structure: `./detectors/<DETECTOR>/checkpoint/pretrained/weights/best.pt`
-
----
-
-### Download Benchmarking Dataset
-
-DeepShield Dataset: [Zenodo link](https://zenodo.org/records/15648378)
-
-Download the dataset and change the corresponding `dataset_path` in `./configs/<DETECTOR>.yaml`.
-
-> The DeepShield dataset is a large-scale benchmark for evaluating the robustness of fake image detection systems. It contains 100,000 images, divided between real and AI-generated content produced using advanced generative models, including StyleGAN, StyleGAN2, StyleGAN3, Stable Diffusion 1.5, 2.1, 3, and XL, as well as Flux 1.0.
->
-> To simulate real-world distortions, 30,000 images were shared on Facebook, X (formerly Twitter), and Telegram, then re-collected to include platform-induced compression and artifacts. This approach ensures that the dataset captures authentic distribution noise and artifacts encountered in real-world scenarios.
-
----
-
-### VirtualEnv
-
-Create a virtual environment using:
-
-```bash
-python -m venv IDFD_VENV
-source IDFD_VENV/bin/activate
-pip install -r requirements.txt
-````
-
-Or use conda:
-
-```bash
-conda env create -f environment.yml
-conda activate IDFD_VENV
-```
-
------
-
-### Download Demo Dataset
-
-You can download the demo dataset from this [link](https://drive.google.com/file/d/134Bw8l9tEC7oZJpTAeMO80QRqgdJfJS9/view?usp=sharing). The demo dataset contains 200 images randomly sampled from the DeepShield Dataset.
-
-Place sample images for quick testing in `demo_images/`, organized by platform and label:
-
-```
-    demo_images/
-            Facebook/
-                    Fake/
-                    Real/
-            PreSocial/
-                    Fake/
-                    Real/
-            Telegram/
-                    Fake/
-                    Real/
-            X/
-                    Fake/
-                    Real/
-```
-
------
-
-## Running Experiments
-
-**1. Run Demo:**
-
-Test all detectors on sample images:
-
-```bash
-python launcher.py --demo --demo-detector all
-```
-
-Test a single detector on sample images:
-
-```bash
-python launcher.py --demo --demo-detector <DETECTOR>
-```
-
-**2. Automated Train-Test (Recommended) on DeepShield Dataset:**
+# Deepfake Detection Library
 
-To run both train and test using a specific `<DETECTOR>`:
+This Space provides a unified interface to test multiple state-of-the-art deepfake detection models on your images.
 
-```bash
-python launcher.py --detector <DETECTOR> --phases both
-```
+## Available Detectors
 
-  - `<DETECTOR>`: One of `CLIP-D`, `NPR`, `P2G`, `R50_TF`, `R50_nodown`
-  - `--phases`: `train`, `test`, or `both`
+- **R50_TF** - ResNet-50 based detector trained on TrueFake dataset
+- **R50_nodown** - ResNet-50 without downsampling operations
+- **CLIP-D** - CLIP-based deepfake detector
+- **P2G** - Prompt2Guard: Conditioned prompt-optimization for continual deepfake detection
+- **NPR** - Neural Posterior Regularization
 
-By doing so, the selected detector will be trained on images generated by StyleGAN2 and StableDiffusionXL and real images from the FORLAB and FFHQ Datasets, **not shared on social networks**.
+## Usage
 
-**3. Manual Train-Test on DeepShield Dataset:**
+1. Upload an image
+2. Select a detector from the dropdown
+3. Click "Detect" to get the prediction
 
-```bash
-python launcher.py --detector <DETECTOR> --phases <PHASE> --config-dir <CONFIG_FILE_PATH> --weights_name <WEIGHTS_NAME>
-```
+The detector will return:
+- **Prediction**: Real or Fake
+- **Confidence**: Model confidence score (0-1)
+- **Elapsed Time**: Processing time
 
-  - `<DETECTOR>`: One of `CLIP-D`, `NPR`, `P2G`, `R50_TF`, `R50_nodown`
-  - `--phases`: `train`, `test`, or `both`
-  - `--config-dir`: Path to the detector config files (default: `configs/`)
-  - `--weights_name`: Model weights name. The default is defined in `configs/<DETECTOR>.yaml` by these lines:
+## Models
 
-<!-- end list -->
-
-```
-training:
-  - data: gan2:pre&sdXL:pre&realFFHQ:pre&realFORLAB:pre
-```
-
-This corresponds to the training subsets used to train a detector (see the "Train on Different Generators from the DeepShield Dataset" section for more information).
-
-**4. Test the model using pretrained weights on the DeepShield Dataset:**
-
-```bash
-python launcher.py --detector <DETECTOR> --phases test --weights_name pretrained
-```
-
-**5. Train the model using a custom weights name on the DeepShield Dataset:**
-
-```bash
-python launcher.py --detector <DETECTOR> --phases train --weights_name <WEIGHTS_NAME>
-```
-
-**6. Perform Detection on Single Images:**
-
-```bash
-python launcher.py --detect --detector <DETECTOR> --image <PATH_TO_IMAGE> --weights <WEIGHTS_NAME> --output <OUTPUT_PATH>
-```
-
-  - `<DETECTOR>`: One of `CLIP-D`, `NPR`, `P2G`, `R50_TF`, `R50_nodown`
-  - `--image`: Path to the input image
-  - `--weights`: Weights name (default: `pretrained`)
-  - `--output`: Path to save detection results (default: `detection_results`)
-
------
-
-## Train on Different Generators from the DeepShield Dataset
-
-To train a detector on generators different from StyleGAN2 and StableDiffusionXL, modify these lines in `configs/<DETECTOR>.yaml`:
-
-```json
-training:
-  - data: gan2:pre&sdXL:pre&realFFHQ:pre&realFORLAB:pre
-```
-
-Currently supported pairs of `(key, sub-dataset)` are:
-
-```
-'gan1':['StyleGAN']
-'gan2':['StyleGAN2']
-'gan3':['StyleGAN3']
-'sd15':['StableDiffusion1.5']
-'sd2':['StableDiffusion2']
-'sd3':['StableDiffusion3']
-'sdXL':['StableDiffusionXL']
-'flux':['FLUX.1']
-'realFFHQ':['FFHQ']
-'realFORLAB':['FORLAB']
-```
-
-And supported pairs of `(key, social)` are:
-
-```
-'pre':[Not Shared on Social Networks],
-'fb': [Facebook]
-'tl': [Telegram]
-'tw': [X Social]
-```
-
-Then, generate the corresponding `split.json` using `python support/json_compile.py` and use it to replace `./split.json`. NOTE: change line 9 `dataset_path=...` in `support/json_compile.py` accordingly. 
-
-## Results & Logs
-
-  - **Results:** Saved in `detectors/<DETECTOR>/results/`
-  - **Logs:** Saved in `logs/` per run and scenario
-
------
-
-## Train/Test on a New Dataset
-
-### Data Organization
-
-Organize your data by platform and label:
-
-```
-    <DATASET_NAME>/
-            Facebook/
-                    Fake/
-                    Real/
-            PreSocial/
-                    Fake/
-                    Real/
-            Telegram/
-                    Fake/
-                    Real/
-            X/
-                    Fake/
-                    Real/
-```
-
-Generate the corresponding `split.json` using `python support/json_compile.py` and use it to replace `./split.json`. NOTE: change line 9 `dataset_path=...` in `support/json_compile.py` accordingly. 
-
-### Split Files
-
-  - **`split.json`:** Main split file for experiments. Format: JSON with `train`/`test` keys and lists of sample IDs.
-  - **`split_demo.json`:** Auto-generated for demo mode, covering all images in `demo_images/`.
-
------
-
-## Additional Configuration Options
-
-  - **YAML Files:** All detectors have a config file in `configs/` (e.g., `CLIP-D.yaml`, `NPR.yaml`).
-  - **Config Options:**
-      - `global`: Dataset path, device, split file, threads, etc.
-      - `detector_args`: Model-specific arguments.
-      - `training`: List of training scenarios.
-      - `testing`: List of test scenarios.
-
------
+All models have been pretrained on images generated with StyleGAN2 and StableDiffusionXL, and real images from the FFHQ Dataset and the FORLAB Dataset.
 
 ## References
 
-[1] D. Cozzolino, G. Poggi, R. Corvi, M. Nießner, and L. Verdoliva,
-“Raising the Bar of AI-generated Image Detection with CLIP,” in 2024
-IEEE/CVF Conference on Computer Vision and Pattern Recognition
-Workshops (CVPRW), pp. 4356–4366, June 2024. ISSN: 2160-7516.
-
-[2]C. Tan, H. Liu, Y. Zhao, S. Wei, G. Gu, P. Liu, and Y. Wei, “Rethinking the Up-Sampling Operations in CNN-Based Generative Network for
-Generalizable Deepfake Detection,” in 2024 IEEE/CVF Conference on
-Computer Vision and Pattern Recognition (CVPR), pp. 28130–28139,
-June 2024. ISSN: 2575-7075.
-
-[3] F. Laiti, B. Liberatori, T. De Min, and E. Ricci, “Conditioned Prompt-Optimization for Continual Deepfake Detection,” in Pattern Recognition (A. Antonacopoulos, S. Chaudhuri, R. Chellappa, C.L. Liu, S. Bhatacharya, and U. Pal, eds.), (Cham), pp. 64–79, Springer Nature Switzerland, 2025.
-
-[4] Dell'Anna, Stefano, Andrea Montibeller, and Giulia Boato. "TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks." arXiv preprint arXiv:2504.20658 (2025).
-
-[5]R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, “On The Detection of Synthetic Images Generated by Diffusion
-Models,” in ICASSP 2023 - 2023 IEEE International Conference on
-Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5, June 2023.
-ISSN: 2379-190X.
+For more information about the implementation and benchmarking, visit the [GitHub repository](https://github.com/truebees-ai/Image-Deepfake-Detectors-Public-Library).
 
-[6] NVlabs, “Flickr faces hq dataset.” https://github.com/NVlabs/ffhq-dataset, n.d. Accessed: 2025-03-04
+## Note
 
-[7] M. Iuliani, M. Fontani, and A. Piva, “A leak in prnu based source
-identification—questioning fingerprint uniqueness,” IEEE Access, vol. 9, pp. 52455–52463, 2021.
+⚠️ Due to file size limitations, model weights need to be downloaded automatically on first use. This may take a few moments.