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Dataset Composition + +This dataset is a multi-domain bennchmark for detecting AI-generated text-rich images from GPT-Image-2. + +The dataset consists of two complementary subsets: + +1. Fake subset: +Images generated by GPT-Image-2 using carefully designed prompts. The prompts are constructed to cover diverse domains and layouts while avoiding reproduction of specific real-world images. + +2. Real subset: +Images sampled from six publicly available datasets. These images are selected as real-world references to provide natural examples of text-rich visual content. + +The final dataset contains 6,109 synthetic images and 5,986 real images. + + + + + + +### Dataset Sources + + + + +- **Paper:** https://arxiv.org/abs/2606.19259 + + +## Dataset Structure + +The dataset is organized hierarchically by image category and authenticity label: + +``` +Data/ +├── AcademicPosters/ +│ ├── 0_real/ +│ └── 1_fake/ +├── CommercialPosters/ +│ ├── 0_real/ +│ └── 1_fake/ +├── InfographicCharts/ +│ ├── 0_real/ +│ └── 1_fake/ +├── Receipts/ +│ ├── 0_real/ +│ └── 1_fake/ +├── Tables/ +│ ├── 0_real/ +│ └── 1_fake/ +└── UIScreenshots/ +├── 0_real/ +└── 1_fake/ +``` + + +### Source Data + + +The Real subset is constructed by sampling images from six publicly available datasets: + +| Subset | Source Dataset | License | +|---|---|---| +| Real/AcademicPosters | [PosterSum](https://huggingface.co/datasets/rohitsaxena/PosterSum) | -- | +| Real/CommercialPosters | [Crello](https://huggingface.co/datasets/cyberagent/crello) | CDLA-Permissive-2.0 | +| Real/InfographicCharts | [ChartGalaxy](https://huggingface.co/datasets/ChartGalaxy/ChartGalaxy) | Apache 2.0 | +| Real/Receipts | [SROIE](https://rrc.cvc.uab.es/?ch=13&com=introduction) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | +| Real/Tables | [PubTables-1M](https://huggingface.co/datasets/bsmock/pubtables-1m) | CDLA-Permissive-2.0 | +| Real/UIScreenshots | [Enrico](https://github.com/luileito/enrico) | MIT | + +Only a subset of images from each dataset is included in this dataset. The original datasets are not redistributed in their entirety. +The included real-world images remain subject to the original licenses of their respective source datasets. Users should refer to the original datasets for license terms and usage conditions. + + + + + + + + +## Citation + +If you find this dataset useful, please cite our paper: + +```bibtex +@misc{wang2026textrich, + title={TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2}, + author={Yijin Wang and Shuyi Wang and Wenhan Zhang and Yuqi Ouyang}, + year={2026}, + eprint={2606.19259}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2606.19259}, +} +``` \ No newline at end of file diff --git a/dataset.csv b/dataset.csv new file mode 100644 index 0000000000000000000000000000000000000000..44296c2cea5d71685ff70f1bf5a300382152cd76 --- /dev/null +++ b/dataset.csv @@ -0,0 +1,12096 @@ +image,category,label +Data/AcademicPosters/0_real/ICLR_2022_5908_Divergence-aware Federated Self-Supervised Learning.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_5918_Relational Surrogate Loss Learning.png,AcademicPosters,0 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Pre-trainin.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6049_Pretraining Text Encoders with Adversarial Mixture of Traini.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6058_Generalizing Few-Shot NAS with Gradient Matching.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6066_Pseudo Numerical Methods for Diffusion Models on Manifolds.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6071_Compositional Training for End-to-End Deep AUC Maximization.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6085_NASViT_ Neural Architecture Search for Efficient Vision Tran.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6098_A Program to Build E_N_-Equivariant Steerable CNNs.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6104_An Agnostic Approach to Federated Learning with Class Imbala.png,AcademicPosters,0 +Data/AcademicPosters/0_real/ICLR_2022_6112_Is Fairness Only Metric Deep_ Evaluating and Addressing 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+Data/UIScreenshots/1_fake/ui_0994.png,UIScreenshots,1 +Data/UIScreenshots/1_fake/ui_0995.png,UIScreenshots,1 +Data/UIScreenshots/1_fake/ui_0996.png,UIScreenshots,1 +Data/UIScreenshots/1_fake/ui_0997.png,UIScreenshots,1 +Data/UIScreenshots/1_fake/ui_0998.png,UIScreenshots,1 +Data/UIScreenshots/1_fake/ui_0999.png,UIScreenshots,1 +Data/UIScreenshots/1_fake/ui_1000.png,UIScreenshots,1 diff --git a/third_party_licenses/CDLA-Permissive-2.0 LICENSE b/third_party_licenses/CDLA-Permissive-2.0 LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..cc0f954b59ba22a5c892e9833b0013aa281f5838 --- /dev/null +++ b/third_party_licenses/CDLA-Permissive-2.0 LICENSE @@ -0,0 +1,35 @@ +Community Data License Agreement - Permissive - Version 2.0 + +This is the Community Data License Agreement - Permissive, Version 2.0 (the "agreement"). Data Provider(s) and Data Recipient(s) agree as follows: + +1. Provision of the Data + +1.1. A Data Recipient may use, modify, and share the Data made available by Data Provider(s) under this agreement if that Data Recipient follows the terms of this agreement. + +1.2. This agreement does not impose any restriction on a Data Recipient's use, modification, or sharing of any portions of the Data that are in the public domain or that may be used, modified, or shared under any other legal exception or limitation. + +2. Conditions for Sharing Data + +2.1. A Data Recipient may share Data, with or without modifications, so long as the Data Recipient makes available the text of this agreement with the shared Data. + +3. No Restrictions on Results + +3.1. This agreement does not impose any restriction or obligations with respect to the use, modification, or sharing of Results. + +4. No Warranty; Limitation of Liability + +4.1. All Data Recipients receive the Data subject to the following terms: + +THE DATA IS PROVIDED ON AN "AS IS" BASIS, WITHOUT REPRESENTATIONS, WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. + +NO DATA PROVIDER SHALL HAVE ANY LIABILITY FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING WITHOUT LIMITATION LOST PROFITS), HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE DATA OR RESULTS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. + +5. Definitions + +5.1. "Data" means the material received by a Data Recipient under this agreement. + +5.2. "Data Provider" means any person who is the source of Data provided under this agreement and in reliance on a Data Recipient's agreement to its terms. + +5.3. "Data Recipient" means any person who receives Data directly or indirectly from a Data Provider and agrees to the terms of this agreement. + +5.4. "Results" means any outcome obtained by computational analysis of Data, including for example machine learning models and models' insights. diff --git a/third_party_licenses/ENRICO LICENSE.txt b/third_party_licenses/ENRICO LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..79648291b0159f0ea73e943ecfbea10085323f50 --- /dev/null +++ b/third_party_licenses/ENRICO LICENSE.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Luis A. Leiva, Asutosh Hota, Antti Oulasvirta + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE.