在生成式人工智能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
AI总结：
本文构建了大规模生成式区域编辑数据集GRE，整合多模态大模型和多种编辑方法，并通过分类、归因和定位三项任务推动生成式图像编辑检测研究。
摘要：
生成式人工智能的加速发展显著提升了生成式区域编辑方法的可行性和有效性。这一演变使得图像篡改更加容易实现，从而加剧了篡改原始图像所传达信息甚至传播虚假信息的风险。因此，迫切需要能够检测被编辑图像的鲁棒方法。然而，缺乏包含丰富且先进的生成式区域编辑方法所编辑图像的综合数据集，对相应检测方法的发展构成了重大障碍。我们致力于通过构建GRE数据集来填补这一空白，这是一个大规模生成式区域编辑数据集，具有以下优势：1）收集真实世界的原始图像，聚焦于两个经常被编辑的场景。2）整合了一条逻辑且模拟的编辑流程，利用多种模态的多个大模型。3）包含具有不同架构的多种编辑方法。4）提供全面的分析任务。我们利用提出的三个任务进行了全面实验：编辑图像分类、编辑方法归因和编辑区域定位，提供了对不同编辑方法的分析以及对相关领域检测方法的评估。我们期望GRE数据集能够促进生成式区域编辑检测领域的进一步研究和探索。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
