TheKernel01/AIGC-Detection-Benchmark

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.