Image Forgery Localization with State Space Models
This addresses the problem of detecting tampered images for applications like digital forensics, but it is incremental as it adapts a new model type (SSMs) to an existing task.
The authors tackled image forgery localization by proposing LoMa, a method using State Space Models (SSMs) to model pixel dependencies with long-range interactions and linear computational complexity, achieving superior performance over CNN-based and Transformer-based state-of-the-art models in experiments.
Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, we propose LoMa, a novel image forgery localization method that leverages the selective SSMs. Specifically, LoMa initially employs atrous selective scan to traverse the spatial domain and convert the tampered image into ordered patch sequences, and subsequently applies multi-directional state space modeling. In addition, an auxiliary convolutional branch is introduced to enhance local feature extraction. Extensive experimental results validate the superiority of LoMa over CNN-based and Transformer-based state-of-the-arts. To our best knowledge, this is the first image forgery localization model constructed based on the SSM-based model. We aim to establish a baseline and provide valuable insights for the future development of more efficient and effective SSM-based forgery localization models. Code is available at https://github.com/multimediaFor/LoMa.