用高熵区域增强相机型号识别，准确率超99%。
Camera Model Identification with SPAIR-Swin and Entropy based Non-Homogeneous Patches
- 结合空间注意力与Swin Transformer捕捉全局局部特征
- 在四个数据集上实现97.46%至100%的图像级准确率
- 聚焦高熵纹理区，提升噪声和压缩痕迹识别效果
源相机型号识别（SCMI）在图像取证中至关重要，可用于真实性验证和版权保护。本文提出SPAIR-Swin模型，融合改进的空间注意力机制与倒残差块（SPAIR）及Swin Transformer，有效捕捉全局与局部特征，强化对噪声模式等伪造痕迹的识别能力。不同于传统方法仅关注均匀区域，我们提出基于熵值的非均匀块选择策略，优先选取富含纹理与高频信息的高熵区域。在Dresden、Vision、Forchheim和Socrates四个基准数据集上的实验表明，SPAIR-Swin在块级准确率达99.45%、98.39%、99.45%、97.46%，图像级准确率达99.87%、99.32%、100%、98.61%。结果表明，包含边缘锐度、噪声和压缩伪影的高熵区域显著提升识别精度。代码将根据请求提供。
原文摘要 · Abstract (English)
Source camera model identification (SCMI) plays a pivotal role in image forensics with applications including authenticity verification and copyright protection. For identifying the camera model used to capture a given image, we propose SPAIR-Swin, a novel model combining a modified spatial attention mechanism and inverted residual block (SPAIR) with a Swin Transformer. SPAIR-Swin effectively captures both global and local features, enabling robust identification of artifacts such as noise patterns that are particularly effective for SCMI. Additionally, unlike conventional methods focusing on homogeneous patches, we propose a patch selection strategy for SCMI that emphasizes high-entropy regions rich in patterns and textures. Extensive evaluations on four benchmark SCMI datasets demonstrate that SPAIR-Swin outperforms existing methods, achieving patch-level accuracies of 99.45%, 98.39%, 99.45%, and 97.46% and image-level accuracies of 99.87%, 99.32%, 100%, and 98.61% on the Dresden, Vision, Forchheim, and Socrates datasets, respectively. Our findings highlight that high-entropy patches, which contain high-frequency information such as edge sharpness, noise, and compression artifacts, are more favorable in improving SCMI accuracy. Code will be made available upon request.
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