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