A new camera model identification method based on color correction features

This paper focuses on source camera model identification technology in the field of digital image forensics. The research goal is to identify the source camera model, and researchers generally use the algorithm design of convolutional neural networks combined with noise residuals. However, traditional features such as noise residuals are easily polluted by noise and compression, which substantially affects the classification accuracy of source camera model identification algorithms for traditional features. Based on existing source camera model identification methods, this paper proposes the use of color correction features as the basic features of source camera model identification for the first time and proposes a new algorithm for source camera model identification based on image color correction features. A convolutional neural network is utilized to extract image color correction features and identify and classify source camera models. This paper has carried out experimental verification on a large-scale dataset, and the source camera model recognition accuracy of the proposed method in this paper can reach 97.23%; the recognition accuracy under compression conditions has reached 91.28%. The experimental results show that the image color correction feature is better than the source camera model in terms of recognition and has great research and application potential in the field of recognition. Additionally, the proposed algorithm is highly robust even after image compression and pollution, outperforming other methods under both original image conditions and compressed image conditions.