| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/test-* |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': real |
| '1': fake |
| - name: generator |
| dtype: |
| class_label: |
| names: |
| '0': Real |
| '1': ADM |
| '2': BigGAN |
| '3': CycleGAN |
| '4': DALLE2 |
| '5': GauGAN |
| '6': GLIDE |
| '7': Midjourney |
| '8': ProGAN |
| '9': SD14 |
| '10': SD15 |
| '11': SDXL |
| '12': StarGAN |
| '13': StyleGAN |
| '14': StyleGAN2 |
| '15': VQDM |
| '16': WhichFaceIsReal |
| '17': Wukong |
| splits: |
| - name: test |
| num_bytes: 29870625563 |
| num_examples: 125026 |
| download_size: 32032878953 |
| dataset_size: 29870625563 |
| license: apache-2.0 |
| task_categories: |
| - image-classification |
| language: |
| - en |
| --- |
| |
| # AIGC Detection Benchmark Dataset |
|
|
| ## 📝 Dataset Description |
|
|
| **Dataset Summary** |
|
|
| The AIGC Detection Benchmark Dataset is a high-quality collection of images and associated metadata designed to benchmark models for detecting and identifying the source of artificially generated content. The dataset contains a mix of real-world images and images generated by a wide array of prominent AI models, including diffusion models (like Stable Diffusion, DALL-E 2, Midjourney, ADM) and GANs (like BigGAN, StyleGAN, ProGAN). |
|
|
| Each image is meticulously labeled under two categories, enabling researchers to tackle two distinct, high-value computer vision tasks: binary real/fake classification and multi-class source model identification. **Note: This specific version of the dataset is designed exclusively for testing and evaluation purposes, with all data consolidated into a single test split.** |
|
|
| **Supported Tasks and Leaderboards** |
|
|
| This dataset directly supports two critical image classification tasks: |
|
|
| |**Task ID**|**Task Name**|**Description**| **Output Classes** | |
| | ----------- | ------------------------------ | ------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| |**Task A**|Binary Veracity Classification|Classifying images as either real or fake.| 2 (real, fake) | |
| |**Task B**|AI Model Source Identification|Identifying the specific AI generation model used for images labeled as AI-Generated.| 18 (Real, ADM, BigGAN, CycleGAN, DALLE2, GauGAN, GLIDE, Midjourney, ProGAN, SD14, SD15, SDXL, StarGAN, StyleGAN, StyleGAN2, VQDM, WhichFaceIsReal, Wukong) | |
|
|
| **Languages** |
|
|
| The descriptive text, including all captions, is in English (en). |
|
|
| ## 🗂️ Data Splits |
|
|
| All instances have been merged into a single test split to serve strictly as an evaluation benchmark. |
|
|
| |**Split**|**Number of Instances**|**Notes**| |
| |---|---|---| |
| |**test**|125,026|Used exclusively for final, unbiased model evaluation and benchmarking.| |
|
|
| ## 💾 Dataset Structure |
|
|
| **Data Instances** |
|
|
| A single data instance consists of an image file and two distinct labels detailing its source and authenticity. |
|
|
| |**Field Name**|**Example Value**|**Description**| |
| |---|---|---| |
| |**image**|`<PIL.Image.Image object>`|The actual image content loaded into a PIL object.| |
| |**label**|`1`|Binary label for authenticity (Real vs. AI-Generated).| |
| |**generator**|`3`|Multi-class label for the specific generation model (or Real).| |
|
|
| **Data Fields** |
|
|
| The dataset contains the following fields: |
|
|
| |**Field Name**|**Data Type**|**Description**| |
| |---|---|---| |
| |**image**|`datasets.Image()`|The actual image content (e.g., .jpg, .png).| |
| |**label**|`datasets.ClassLabel`|Task A: Binary label for image veracity.| |
| |**generator**|`datasets.ClassLabel`|Task B: Label specifying the generation source/model.| |
|
|
| ## 🏷️ Label Definitions |
|
|
| The two label fields use the following mappings: |
|
|
| **`label` (Binary Veracity Classification)** |
|
|
| |**Label**|**Value**|**Description**| |
| |---|---|---| |
| |**real**|`0`|Image is a real photograph/non-AI generated.| |
| |**fake**|`1`|Image was created by an AI generation model.| |
|
|
| **`generator` (Model Source Identification)** |
|
|
| | **Label** | **Value** | **Description** | |
| | ------------------- | --------- | --------------------------------------------------------- | |
| | **Real** | `0` | Real image (no AI generation involved). | |
| | **ADM** | `1` | Generated by Ablated Diffusion Model (Guided Diffusion). | |
| | **BigGAN** | `2` | Generated by BigGAN. | |
| | **CycleGAN** | `3` | Generated by CycleGAN. | |
| | **DALLE2** | `4` | Generated by OpenAI's DALL-E 2. | |
| | **GauGAN** | `5` | Generated by GauGAN (SPADE). | |
| | **GLIDE** | `6` | Generated by GLIDE. | |
| | **Midjourney** | `7` | Generated by Midjourney. | |
| | **ProGAN** | `8` | Generated by ProGAN (Progressive GAN). | |
| | **SD14** | `9` | Generated by Stable Diffusion 1.4. | |
| | **SD15** | `10` | Generated by Stable Diffusion 1.5. | |
| | **SDXL** | `11` | Generated by Stable Diffusion XL. | |
| | **StarGAN** | `12` | Generated by StarGAN. | |
| | **StyleGAN** | `13` | Generated by StyleGAN. | |
| | **StyleGAN2** | `14` | Generated by StyleGAN2. | |
| | **VQDM** | `15` | Generated by Vector Quantized Diffusion Model. | |
| | **WhichFaceIsReal** | `16` | Real human face sourced from the WhichFaceIsReal dataset. | |
| | **Wukong** | `17` | Generated by the Wukong diffusion model. | |
|
|
| ## 🔗 Sources |
|
|
| - **Original dataset**: [Ekko-zn/AIGCDetectBenchmark](https://github.com/Ekko-zn/AIGCDetectBenchmark). |
