学习通用特征以实现可泛化的图像伪造定位
Learning Universal Features for Generalizable Image Forgery Localization
AI总结：
本文提出可泛化图像伪造定位方法GIFL，通过学习原始内容中的通用特征而非特定伪造痕迹来检测未见伪造，并构建深度生成编辑数据集，在未见伪造检测上超越现有方法。
摘要：
近年来，先进的图像编辑与生成方法快速发展，使得检测和定位伪造图像内容变得越来越困难。现有的大多数图像伪造检测方法依赖于识别图像中留下的编辑痕迹。然而，由于不同伪造方式的痕迹各不相同，这些方法能够识别训练数据中包含的熟悉伪造类型，却难以处理未见过的伪造类型。为此，我们提出了一种可泛化图像伪造定位（Generalizable Image Forgery Localization，GIFL）方法。模型一旦完成训练，即可同时检测已见和未见的伪造内容，为生成式人工智能时代应对虚假信息提供了更实用、更高效的解决方案。我们的方法重点是从原始未篡改内容中学习通用特征，而不是学习特定伪造类型的痕迹；这些原始内容特征在不同伪造类型之间相对一致，因此可以作为通用特征来定位未见过的伪造。此外，由于现有图像伪造数据集仍以传统手工伪造为主，我们构建了一个新数据集，其中包含由多种流行的深度生成图像编辑方法编辑的图像，以进一步推动对深度生成模型篡改图像检测的研究。大量实验结果表明，所提方法在检测未见伪造方面优于当前最先进的方法，并且在已见伪造检测上也展现出具有竞争力的结果。代码和数据集可在 https://github.com/ZhaoHengrun/GIFL 获取。
英文摘要：
In recent years, advanced image editing and generation methods have rapidly evolved, making detecting and locating forged image content increasingly challenging. Most existing image forgery detection methods rely on identifying the edited traces left in the image. However, because the traces of different forgeries are distinct, these methods can identify familiar forgeries included in the training data but struggle to handle unseen ones. In response, we present an approach for Generalizable Image Forgery Localization (GIFL). Once trained, our model can detect both seen and unseen forgeries, providing a more practical and efficient solution to counter false information in the era of generative AI. Our method focuses on learning general features from the pristine content rather than traces of specific forgeries, which are relatively consistent across different types of forgeries and therefore can be used as universal features to locate unseen forgeries. Additionally, as existing image forgery datasets are still dominated by traditional hand-crafted forgeries, we construct a new dataset consisting of images edited by various popular deep generative image editing methods to further encourage research in detecting images manipulated by deep generative models. Extensive experimental results show that the proposed approach outperforms state-of-the-art methods in the detection of unseen forgeries and also demonstrates competitive results for seen forgeries. The code and dataset are available at https://github.com/ZhaoHengrun/GIFL.
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。
