Shallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network
This addresses the societal impact of forged images by providing a solution for both types of manipulations, though it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of localizing manipulated pixels in both shallowfake and deepfake images, achieving higher inference accuracy compared to state-of-the-art methods.
Image manipulation localization is a critical research task, given that forged images may have a significant societal impact of various aspects. Such image manipulations can be produced using traditional image editing tools (known as "shallowfakes") or advanced artificial intelligence techniques ("deepfakes"). While numerous studies have focused on image manipulation localization on either shallowfake images or deepfake videos, few approaches address both cases. In this paper, we explore the feasibility of using a deep learning network to localize manipulations in both shallow- and deep-fake images, and proposed a solution for such purpose. To precisely differentiate between authentic and manipulated pixels, we leverage the Vision Mamba network to extract feature maps that clearly describe the boundaries between tampered and untouched regions. To further enhance this separation, we propose a novel Guided Graph Neural Network (G-GNN) module that amplifies the distinction between manipulated and authentic pixels. Our evaluation results show that our proposed method achieved higher inference accuracy compared to other state-of-the-art methods.