Splicing forgery localization via noise fingerprint incorporated with CFA configuration. (November 2022)

• Record Type: Journal Article
• Authors: Liu, Lei; Sun, Peng; Lang, Yubo; Li, Jingjiao; Shi, Shaopei
• Is Part Of: Forensic science international. Volume 340(2022)
• Publication Date: 2022-11
• Subjects: Digital image forensics -- Forgery localization -- Noise estimation -- CFA configuration; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2022.111464
• Languages: English
• ISSNs: 0379-0738

Abstract

Abstract: Noise is the inherent intrinsic fingerprint in digital images and is often used for forgery localization. Most noise-based methods assume that the noise is similar over the whole image and can be considered as white Gaussian noise. However, the noise is different in various regions, which degrade the performance of these noise-based methods. To reduce the impact of impractical assumptions, in this paper, we propose an effective noise fingerprint incorporated with CFA configuration for splicing forgery localization. The noise of interpolated pixels is expected to be suppressed after interpolation, and the relationship between the noise levels of adjacent acquired and interpolated pixels is only related to the interpolation algorithm, which is constant in the original image. We utilize a dual tree wavelet based denoising algorithm to extract the noise from the green channel and compute the standard deviation of the noise for acquired and interpolated pixels, respectively. The noise level of acquired and interpolated pixels are then obtained by the geometric mean of the noise standard deviations. Finally, the ratio of noise levels between acquired and interpolated pixels can be a fingerprint to locate tampered regions. Experiments conducted on publicly available databases demonstrate that the proposed approach outperforms previous methods for detecting splice tampering. Moreover, the proposed method is robust to Gaussian filtering and JPEG compression attacks. Highlights: Incorporating CFA configuration into noise fingerprint provides the advantage of low false alarm rate of CFA method. The features used in the statistical methods are analyzed and compared according to the forgery location target. The experiments are mainly conducted on publicly available databases rather than simulating tampered images.