DETER：检测编辑区域以遏制生成式篡改
DETER: Detecting Edited Regions for Deterring Generative Manipulations
AI总结：
针对现有深度伪造数据集存在虚假相关性、跟不上生成式AI发展的问题，研究团队推出包含30万张多类型篡改图像的大规模数据集DETER，经实验验证其伪造内容更逼真，可为区域伪造检测研究提供新基准与方向。
摘要：
近年来，生成式AI能力大幅提升，再度引发人们对生成数据（即“深度伪造”）可能被恶意使用的担忧。然而，深度伪造数据集的发展未能跟上生成式AI的进步步伐，无法支撑开发出能在现实场景中切实为人类用户发出预警的深度伪造检测技术。现有数据集通常使用基于GAN的模型，且总是对相似的人脸区域进行编辑，从而引入了虚假相关性。\n为弥补这些缺陷，我们推出了DETER——一个用于检测图像编辑区域、遏制现代先进生成式篡改的大规模数据集。DETER包含30万张由4个最先进生成器处理得到的篡改图像，涵盖三种编辑操作：人脸交换（一种标准的粗粒度图像篡改）、图像修复（深度伪造数据集中的一种新型篡改方式）以及属性编辑（一种精细的细粒度篡改）。人脸交换和属性编辑是在眼睛、鼻子等相似的人脸区域进行，而图像修复操作可在随机图像区域执行，消除了以往数据集中存在的虚假相关性。我们对图像进行了细致的后处理，以确保DETER中的深度伪造内容看起来逼真，且人类研究证实，人类在DETER上的深度伪造检测率比在其他伪造数据集上低20.4%。\n借助该数据集，我们利用其丰富的标注和改进的基准协议开展了大量实验与细分分析，揭示了开发可靠的区域伪造检测模型的未来方向和新一轮挑战。
英文摘要：
Generative AI capabilities have grown substantially in recent years, raising renewed concerns about potential malicious use of generated data, or "deep fakes". However, deep fake datasets have not kept up with generative AI advancements sufficiently to enable the development of deep fake detection technology which can meaningfully alert human users in real-world settings. Existing datasets typically use GAN-based models and introduce spurious correlations by always editing similar face regions. To counteract the shortcomings, we introduce DETER, a large-scale dataset for DETEcting edited image Regions and deterring modern advanced generative manipulations. DETER includes 300,000 images manipulated by four state-of-the-art generators with three editing operations: face swapping (a standard coarse image manipulation), inpainting (a novel manipulation for deep fake datasets), and attribute editing (a subtle fine-grained manipulation). While face swapping and attribute editing are performed on similar face regions such as eyes and nose, the inpainting operation can be performed on random image regions, removing the spurious correlations of previous datasets. Careful image post-processing is performed to ensure deep fakes in DETER look realistic, and human studies confirm that human deep fake detection rate on DETER is 20.4% lower than on other fake datasets. Equipped with the dataset, we conduct extensive experiments and break-down analysis using our rich annotations and improved benchmark protocols, revealing future directions and the next set of challenges in developing reliable regional fake detection models.
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。
