The accelerated advancement of generative AI significantly enhance the
viability and effectiveness of generative regional editing methods. This
evolution render the image manipulation more accessible, thereby intensifying
the risk of altering the conveyed information within original images and even
propagating misinformation. Consequently, there exists a critical demand for
robust capable of detecting the edited images. However, the lack of
comprehensive dataset containing images edited with abundant and advanced
generative regional editing methods poses a substantial obstacle to the
advancement of corresponding detection methods.
We endeavor to fill the vacancy by constructing the GRE dataset, a
large-scale generative regional editing dataset with the following advantages:
1) Collection of real-world original images, focusing on two frequently edited
scenarios. 2) Integration of a logical and simulated editing pipeline,
leveraging multiple large models in various modalities. 3) Inclusion of various
editing approaches with distinct architectures. 4) Provision of comprehensive
analysis tasks. We perform comprehensive experiments with proposed three tasks:
edited image classification, edited method attribution and edited region
localization, providing analysis of distinct editing methods and evaluation of
detection methods in related fields. We expect that the GRE dataset can promote
further research and exploration in the field of generative region editing
detection.

构建了 GRE 数据集，该数据集包括原始图片和使用多个大型模型进行编辑的多种编辑方式，旨在推动生成区域编辑检测领域的进一步研究和探索。

在生成 AI 革命时代重新思考图像编辑检测

Rethinking Image Editing Detection in the Era of Generative AI  Revolution

This study investigates the robustness of image classifiers to text-guided
corruptions. We utilize diffusion models to edit images to different domains.
Unlike other works that use synthetic or hand-picked data for benchmarking, we
use diffusion models as they are generative models capable of learning to edit
images while preserving their semantic content. Thus, the corruptions will be
more realistic and the comparison will be more informative. Also, there is no
need for manual labeling and we can create large-scale benchmarks with less
effort. We define a prompt hierarchy based on the original ImageNet hierarchy
to apply edits in different domains. As well as introducing a new benchmark we
try to investigate the robustness of different vision models. The results of
this study demonstrate that the performance of image classifiers decreases
significantly in different language-based corruptions and edit domains. We also
observe that convolutional models are more robust than transformer
architectures. Additionally, we see that common data augmentation techniques
can improve the performance on both the original data and the edited images.
The findings of this research can help improve the design of image classifiers
and contribute to the development of more robust machine learning systems. The
code for generating the benchmark will be made available online upon
publication.

本研究探讨了图像分类器对以文字为导向的失真的鲁棒性，并利用扩散模型对图像进行不同域的编辑，研究结果表明： 在不同语言为基础的失真和编辑域中，图像分类器的性能显著下降，卷积模型比变压器架构更为鲁棒，同时，常见的数据扩充技术可以提高原始数据和编辑图像的性能。