Image Forgery Detection Based on Noise Inspection: Analysis and Refinement of the Noisesniffer Method
Marina Gardella, Pablo Musé, Miguel Colom, Jean-Michel Morel
published
2024-04-04
reference
Marina Gardella, Pablo Musé, Miguel Colom, and Jean-Michel Morel, Image Forgery Detection Based on Noise Inspection: Analysis and Refinement of the Noisesniffer Method, Image Processing On Line, 14 (2024), pp. 86–115. https://doi.org/10.5201/ipol.2024.462

Communicated by Rafael Grompone and Yanhao Li
Demo edited by Marina Gardella and Pablo Musé

Abstract

Images undergo a complex processing chain from the moment light reaches the camera's sensor until the final digital image is delivered. Each of its operations leaves traces on the noise model which enable forgery detection through noise analysis. In this article, we describe the Noisesniffer method [Gardella et al., Noisesniffer: a Fully Automatic Image Forgery Detector Based on Noise Analysis, IEEE International Workshop on Biometrics and Forensics, 2021]. This method estimates for each image a background stochastic model which makes it possible to detect local noise anomalies characterized by their number of false alarms. We improve on the original formulation of the method by introducing a region-growing algorithm to detect local deviations from the background model. Results show that the proposed method outperforms the previous version as well as the state of the art.

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