(https://ieeexplore.ieee.org/document/8081213)
2018-2019 Department of Information Engineering and Mathematics, University of Siena, Italy.
Authors: Mauro Barni, Ehsan Nowroozi Personal Website: www.enowroozi.com, Benedetta Tondi (Ehsan.Nowroozi65@gmail.com)
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@INPROCEEDINGS{8081213, author={M. {Barni} and E. {Nowroozi} and B. {Tondi}}, booktitle={2017 25th European Signal Processing Conference (EUSIPCO)}, title={Higher-order, adversary-aware, double JPEG-detection via selected training on attacked samples}, year={2017}, volume={}, number={}, pages={281-285}, keywords={data compression;image coding;security of data;support vector machines;adversary-aware double JPEG detector;JPEG compression steps;heterogeneous processing;SVM classifier;double JPEG detection;counter-forensic attacks;C-F attacks;Image coding;Detectors;Transform coding;Training;Support vector machines;Discrete cosine transforms;Feature extraction}, doi={10.23919/EUSIPCO.2017.8081213}, ISSN={2076-1465}, month={Aug},}
In this paper we present an adversary-aware double JPEG detector which is capable of detecting the presence of two JPEG compression steps even in the presence of heterogeneous processing and counter-forensic (C-F) attacks. The detector is based on an SVM classifier fed with a large number of features and trained to recognise the traces left by double JPEG detection in the presence of attacks. Since it is not possible to train the SVM on all possible kinds of processing and C-F attacks, a selected set of images, manipulated with a limited number of attacks is added to the training set. The processing tools used for training are chosen among those that proved to be most effective in disabling double JPEG detection. Experimental results prove that training on such a kind of most powerful attacks allows good detection in the presence of a much wider variety of attacks and processing. Good performance are retained over a wide range of compression quality factors.