Camera model identification based on dual-path enhanced ConvNeXt network and patches selected by uniform local binary pattern.

With the rapid advancement of multimedia technologies, there is a growing demand for reliable methods to verify image integrity. Camera model identification, a passive approach aiming to determine the specific capturing device model, has garnered considerable attention in the field of source camera forensics. In this paper, we first propose a novel patch selection method that enhances the diversity of training data by utilizing the uniform local binary pattern operator to reveal spatial textual information. Secondly, we introduce a complex dual-path enhanced ConvNeXt network for camera model identification, effectively leveraging the multi-frequency information present in the image. Notably, our network demonstrates the ability to learn camera model-related features without relying on a residual prediction module. Finally, extensive experimental results on both Dresden and Vision datasets shown that the proposed network outperforms several state-of-the-art methods in both teams of identification accuracy and computational efficiency. • Proposing a patch selection method of the uniform local binary pattern operator. • Introducing a dual-path enhanced ConvNeXt network for camera model identification. • Obtaining robust results on the Dresden and Vision datasets. [ABSTRACT FROM AUTHOR]