Camera Model Identification with SPAIR-Swin and Entropy based Non-Homogeneous Patches
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
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.
