Rethinking Image Editing Detection in the Era of Generative AI
  Revolution
Zhihao Sun Haipeng Fang Xinying Zhao Danding Wang Juan Cao
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.