13 April 2018 Non-parametric adaptative JPEG fragments carving
Author Affiliations +
Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106962D (2018) https://doi.org/10.1117/12.2310079
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
The most challenging JPEG recovery tasks arise when the file header is missing. In this paper we propose to use a two layer machine learning model to restore headerless JPEG images. We first build a classifier able to identify the structural properties of the images/fragments and then use an AutoEncoder (AE) to learn the fragment features for the header prediction. We define a JPEG universal header and the remaining free image parameters (Height, Width) are predicted with a Gradient Boosting Classifier. Our approach resulted in 90% accuracy using the manually defined features and 78% accuracy using the AE features.
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Sabrina Cherifa Amrouche, Sabrina Cherifa Amrouche, Dalila Salamani, Dalila Salamani, } "Non-parametric adaptative JPEG fragments carving", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962D (13 April 2018); doi: 10.1117/12.2310079; https://doi.org/10.1117/12.2310079


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