Image forgery detection and localization (IFDL) is of vital importance as forged images can spread misinformation that poses potential threats to our daily life. 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 ConvNeXt-UperNet along with Edge-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.

As manipulating images may lead to misinterpretation of the visual content, addressing the image forgery detection and localization (IFDL) problem has drawn serious public concerns. In this work, we propose a simple assumption that the effective forensic method should focus on the mesoscopic properties of images. Base on the assumption, a novel two-stage self-supervised framework leveraging the diffusion model for IFDL task, i.e., DiffForensics, is proposed in this paper. The DiffForensics begins with self-supervised denoising diffusion paradigm equipped with the module of encoder-decoder structure, by freezing the pre-trained encoder (e.g., in ADE-20K) to inherit macroscopic features for general image characteristics, while encour-aging the decoder to learn microscopic feature represen-tation of images, enforcing the whole model to focus the mesoscopic representations. The pre-trained model as a prior, is then further fine-tuned for IFDL task with the customized Edge Cue Enhancement Module (ECEM), which progressively highlights the boundary features within the manipulated regions, thereby refining tampered area local-ization with better precision. Extensive experiments on several public challenging datasets demonstrate the effectiveness of the proposed method compared with other state-of-the-art methods. The proposed DiffForensics could significantly improve the model's capabilities for both accurate tamper detection and precise tamper localization while con-currently elevating its generalization and robustness.