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Communication Dans Un Congrès Année : 2020

An Adaptive Neural Network for Unsupervised Mosaic Consistency Analysis in Image Forensics

Résumé

Automatically finding suspicious regions in a potentially forged image by splicing, inpainting or copy-move remains a widely open problem. Blind detection neural networks trained on benchmark data are flourishing. Yet, these methods do not provide an explanation of their detections. The more traditional methods try to provide such evidence by pointing out local inconsistencies in the image noise, JPEG compression, chromatic aberration, or in the mosaic. In this paper we develop a blind method that can train directly on unlabelled and potentially forged images to point out local mosaic inconsistencies. To this aim we designed a CNN structure inspired from demosaicing algorithms and directed at classifying image blocks by their position in the image modulo (2 × 2). Creating a diversified benchmark database using varied demosaicing methods, we explore the efficiency of the method and its ability to adapt quickly to any new data.
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Dates et versions

hal-03907110 , version 1 (19-12-2022)

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Quentin Bammey, Rafael Grompone von Gioi, Jean-Michel Morel. An Adaptive Neural Network for Unsupervised Mosaic Consistency Analysis in Image Forensics. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2020, Seattle, United States. pp.14182-14192, ⟨10.1109/CVPR42600.2020.01420⟩. ⟨hal-03907110⟩
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