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Image pre-processing detection: Evaluation of Benford's law, spatial and frequency domain feature performance

  • This Paper proposes a novel method for the blind detection of image pre-processing techniques by means of statistical pattern recognition in image forensics. The technique is intended to detect sensor intrinsic pre-processing steps as well as manually applied filters. We have exemplary chosen 6 pre-processing filters with different parameter settings. The concept utilizes 29 image features which are supposed to allow for a reliable model creation during supervised learning. The evaluation of theThis Paper proposes a novel method for the blind detection of image pre-processing techniques by means of statistical pattern recognition in image forensics. The technique is intended to detect sensor intrinsic pre-processing steps as well as manually applied filters. We have exemplary chosen 6 pre-processing filters with different parameter settings. The concept utilizes 29 image features which are supposed to allow for a reliable model creation during supervised learning. The evaluation of the trained models indicates average accuracies between 82.50 and 94.53%. The investigation of image data from 8 sensors leads to the detection of credible pre-processing filters. Those results adumbrate that our method might be suitable to prove the authenticity of the data origin and the integrity of image data based on the detected preprocessing techniques. The preliminary evaluation for manually applied filters yields recognition accuracies between 39.09% (14 classes) and 53.33% (7 classes).show moreshow less

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Metadaten
Author:Tom Neubert, Mario Hildebrandt, Jana Dittmann
URL:http://ieeexplore.ieee.org/document/7528405/
DOI:https://doi.org/10.1109/SPLIM.2016.7528405
ISBN:978-1-4673-8917-4
Parent Title (English):Konferenz: 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)/ Titel: 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)
Publisher:IEEE
Place of publication:Aalborg, Denmark
Document Type:Conference Proceeding
Language:English
Year of Publishing:2016
Date of Publication (online):2024/02/14
Release Date:2024/02/20
Page Number:5
First Page:1
Last Page:5
Institutes:Fachbereich Informatik und Medien
University Bibliography:Hochschulbibliografie
Licence (German):Creative Commons - CC BY - Namensnennung 4.0
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