metadata
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: cross_test
num_bytes: 268272318
num_examples: 3621
download_size: 268291289
dataset_size: 268272318
configs:
- config_name: default
data_files:
- split: cross_test
path: data/cross_test-*
AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics
This repository provides the processed AutoSplice benchmark dataset for image forgery detection and manipulation localization, formatted consistently with other benchmarks such as KhangTruong/CocoGlide, KhangTruong/OpenSDID, KhangTruong/Casia-v2.0, and KhangTruong/Diffseg30k.
Dataset Overview
- Dataset Name: AutoSplice (Forged_JPEG100 canonical baseline)
- Split:
cross_test - Total Samples: 3621
- Generator: DALL-E 2 language-image model guided by contextual text prompts
- Columns:
image(Image): The tampered image (lossless JPEG quality 100).mask(Image): Single-channel ground-truth binary mask (0= pristine / background,255= tampered / spliced region). Samples with only black masks (no tampered regions) have been removed.
Usage
from datasets import load_dataset
dataset = load_dataset("KhangTruong/AutoSplice", split="cross_test")
sample = dataset[0]
image = sample["image"]
mask = sample["mask"]
print(image.size, mask.size)
Citation
@inproceedings{jia2023autosplice,
title={AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics},
author={Jia, Shan and Huang, Mingzhen and Zhou, Zhou and Ju, Yan and Cai, Jialing and Lyu, Siwei},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
pages={893--903},
year={2023}
}
License & Usage Notes
Released for non-commercial research and educational purposes in accordance with the original AutoSplice dataset terms.