用状态空间模型定位图像篡改，速度更快精度更高
Image Forgery Localization with State Space Models
- 引入可选扫描的空域遍历，将图像转为序列处理
- 在多个方向上建模长程依赖，定位准确率超主流方法
- 适合需要高效高精度的图像真实性检测场景
图像篡改定位的关键在于对像素依赖关系的建模。现有方法多依赖卷积神经网络（CNN）或基于Transformer的模型，但往往受限于感受野不足或计算开销过大。近期出现的状态空间模型（SSM），如Mamba，既能有效建模长程依赖，又保持线性计算复杂度。本文提出LoMa，一种基于选择性SSM的新型图像篡改定位方法。具体而言，LoMa首先利用空洞可选扫描遍历图像空间，将篡改图像转换为有序块序列，再进行多方向状态空间建模；同时引入辅助卷积分支增强局部特征提取。大量实验验证了LoMa在精度和效率上均优于基于CNN和Transformer的先进方法。据我们所知，这是首个基于SSM构建的图像篡改定位模型。本工作旨在建立基准并为未来更高效、更有效的SSM基篡改定位模型提供启示。代码已公开于https://github.com/multimediaFor/LoMa。
原文摘要 · Abstract (English)
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.
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