论文标题
学习功能分离和动态融合，用于重新捕获图像法医
Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic
论文作者
论文摘要
图像重新捕获严重打破了人工智能（AI）系统的公平性，该系统通过重新捕获他人的图像来欺骗系统。大多数现有的重新捕获模型只能基于使用固定的电子设备的模拟重新安装图像的数据集来解决一种基于模拟的重新捕获图像的数据集的单个重新捕获模式（例如，Moire，Edge，Tratifact等）。在本文中，我们将图像重新定义为图像恢复识别的四种模式，即Moire重新捕获，边缘重新捕获，伪影重新捕获和其他重新捕获。同时，我们提出了一种新颖的特征分离和动态融合（FDDF）模型，以适应地学习最有效的重新接收特征表示，以涵盖不同的重新捕获模式识别。此外，我们收集了包含各种重新捕获模式的大规模真实场景通用重新捕获（RUR）数据集，大约是先前发布的数据集数量的五倍。据我们所知，我们是第一个提出一个通用模型和一个通用现场的大规模数据集，用于重新捕获的图像法医。广泛的实验表明，我们提出的FDDF可以在RUR数据集上实现最先进的性能。
Image recapture seriously breaks the fairness of artificial intelligent (AI) systems, which deceives the system by recapturing others' images. Most of the existing recapture models can only address a single pattern of recapture (e.g., moire, edge, artifact, and others) based on the datasets with simulated recaptured images using fixed electronic devices. In this paper, we explicitly redefine image recapture forensic task as four patterns of image recapture recognition, i.e., moire recapture, edge recapture, artifact recapture, and other recapture. Meanwhile, we propose a novel Feature Disentanglement and Dynamic Fusion (FDDF) model to adaptively learn the most effective recapture feature representation for covering different recapture pattern recognition. Furthermore, we collect a large-scale Real-scene Universal Recapture (RUR) dataset containing various recapture patterns, which is about five times the number of previously published datasets. To the best of our knowledge, we are the first to propose a general model and a general real-scene large-scale dataset for recaptured image forensic. Extensive experiments show that our proposed FDDF can achieve state-of-the-art performance on the RUR dataset.
