Recovering information from pixelized credentials
Abstract
Pixelation is a common technique to redact sensitive information like credentials in images. In this paper, we propose a system that is able to recover information from pixelized text. Our contribution consists of a neural network as well as a generic pipeline that generates a realistic training dataset considering flexible specifications including wordlists, fonts, font sizes and letter spacings. The contributed neural network is a composition of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN) using Long short-term memory (LSTM) and a Connectionist Temporal Classification (CTC) layer to decode sequences of characters. With our approach, we achieve a Label Error Rate (LER) under 50% when taking pixelation block sizes of up to 8 × 8 pixels on a 22pt font into account. Thereby, our results indicate that pixelation of sensitive data does not satisfy common privacy standards.
- Citation
- BibTeX
Garske, V. & Noack, A.,
(2022).
Recovering information from pixelized credentials.
In:
Christian Wressnegger, D. R.
(Hrsg.),
GI SICHERHEIT 2022.
Gesellschaft für Informatik, Bonn.
(S. 129-141).
DOI: 10.18420/sicherheit2022_08
@inproceedings{mci/Garske2022,
author = {Garske, Viktor AND Noack, Andreas},
title = {Recovering information from pixelized credentials},
booktitle = {GI SICHERHEIT 2022},
year = {2022},
editor = {Christian Wressnegger, Delphine Reinhardt} ,
pages = { 129-141 } ,
doi = { 10.18420/sicherheit2022_08 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
author = {Garske, Viktor AND Noack, Andreas},
title = {Recovering information from pixelized credentials},
booktitle = {GI SICHERHEIT 2022},
year = {2022},
editor = {Christian Wressnegger, Delphine Reinhardt} ,
pages = { 129-141 } ,
doi = { 10.18420/sicherheit2022_08 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
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More Info
ISBN: 978-3-88579-717-3
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2022
Language:
(en)
