[IPOL announce] new article: Image Forgery Detection Based on Noise Inspection: Analysis and Refinement of the Noisesniffer Method

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2024/462/

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

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.