How can digital forgeries be detected without relying on linear interpolation assumptions? This paper proposes a color filter array (CFA)-based forgery localization method independent of linear assumption. The probability of an interpolated pixel value falling within the range of its neighboring acquired pixel values is computed. This probability serves as a means of discerning the presence and absence of CFA artifacts, as well as distinguishing between various interpolation techniques. Subsequently, curvature is employed in the analysis to select suitable features for generating the tampering probability map. Experimental results on the Columbia and Korus datasets indicate that the proposed method outperforms the state-of-the-art methods and is also more robust to various attacks, such as noise addition, Gaussian filtering, and JPEG compression with a quality factor of 90. The findings suggest that the method offers enhanced robustness against various attacks, providing a more reliable approach for identifying image tampering in digital forensics.
Published in IET Signal Processing, this paper aligns with the journal's focus on advancing signal processing techniques for various applications. By presenting a new method for detecting image forgeries, the study contributes to the journal's ongoing exploration of innovative approaches to image analysis and security. The research addresses a critical challenge in digital forensics and offers a potential solution for enhancing image authentication.