Datasets:

TheKernel01
/

AIGC-Detection-Benchmark

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.