Abstract
Image forgery detection and localization (IFDL) is of vital importance as forged images can spread misinformation that poses potential threats to our daily lives. However, previous methods still struggled to effectively handle forged images processed with diverse forgery operations in real-world scenarios. In this paper, we propose a novel Reinforced Multi-teacher Knowledge Distillation (Re-MTKD) framework for the IFDL task, structured around an encoder-decoder \textbf{C}onvNeXt-\textbf{U}perNet along with \textbf{E}dge-Aware Module, named Cue-Net. First, three Cue-Net models are separately trained for the three main types of image forgeries, i.e., copy-move, splicing, and inpainting, which then serve as the multi-teacher models to train the target student model with Cue-Net through self-knowledge distillation. A Reinforced Dynamic Teacher Selection (Re-DTS) strategy is developed to dynamically assign weights to the involved teacher models, which facilitates specific knowledge transfer and enables the student model to effectively learn both the common and specific natures of diverse tampering traces. Extensive experiments demonstrate that, compared with other state-of-the-art methods, the proposed method achieves superior performance on several recently emerged datasets comprised of various kinds of image forgeries.
Abstract (translated)
图像伪造检测和定位（IFDL）至关重要，因为伪造的图片可以传播错误信息，对我们的日常生活造成潜在威胁。然而，先前的方法在处理真实场景中经过多种伪造操作处理的伪造图片时仍然存在困难。本文提出了一种新的强化多教师知识蒸馏（Re-MTKD）框架来解决IFDL任务，该框架围绕编码器-解码器**C**onvNeXt-**U**perNet以及**E**dge-Aware Module构建，命名为Cue-Net。 首先，分别训练三个Cue-Net模型以处理三种主要类型的图像伪造：复制粘贴、拼接和填充。这些模型作为多教师模型参与自我知识蒸馏过程来培训目标学生模型。此外，开发了一种强化动态教师选择（Re-DTS）策略，用于根据具体情况为涉及的教师模型分配权重，这有助于特定的知识转移，并使学生模型能够有效地学习各种篡改痕迹中普遍和特定的性质。 大量的实验表明，与现有的其他先进方法相比，所提出的方法在包含多种类型图像伪造的几个最近出现的数据集上取得了优越的性能。
URL
https://arxiv.org/abs/2504.05224
