With rapid advancements in image synthesis and deep generative models, the creation, manipulation, and distribution of high-quality fake images have become increasingly prevalent. This trend threatens public trust, individual reputations, and safety due to the proliferation of counterfeit images. Numerous methods, particularly those leveraging deep neural networks, have been developed to detect fake images. While these methods have significantly improved the accuracy of image authenticity detection, there remains substantial room for improvement. In this paper, we propose a novel solution that tracks the presence of the Bayer pattern in images to determine their authenticity as real camera-captured images. Modern digital cameras use integrated color sensors to capture light intensity and convert these variations into digital signals. Our method exploits the color discrepancies generated by the demosaicing process in authentic images. Performance evaluations demonstrated that our approach achieves 97.96% accuracy, outperforming current state-of-the-art detection methods in identifying image authenticity.