Authors:
Vít Listík
1
;
Jan Šedivý
2
and
Václav Hlaváč
2
Affiliations:
1
Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics, Prague 6, Technická 2, Czech Republic
;
2
Czech Institute of Informatics, Robotics and Cybernetics, Prague 6, Jugoslávských partyzánů 1580/3, Czech Republic
Keyword(s):
Spam, Email, ResNet, Image, Classification, Convolutional Neural Network.
Abstract:
The problem with email image spam classification is known from the year 2005. There are several approaches
to this task. Lately, those approaches use convolutional neural networks (CNN). We propose a novel approach
to the image spam classification task. Our approach is based on CNN and transfer learning, namely Resnet v1
used for semantic feature extraction and one layer Feedforward Neural Network for classification. We have
shown that this approach can achieve state-of-the-art performance on publicly available datasets. 99% F1-
score on two datasets (Dredze et al., 2007), Princeton and 96% F1-score on the combination of these datasets.
Due to the availability of GPUs, this approach may be used for just-in-time classification in anti-spam systems
handling huge amounts of emails. We have observed also that mentioned publicly available datasets are no
longer representative. We overcame this limitation by using a much richer dataset from a one-week long real
traffic of the freemail prov
ider Email.cz. The training data annotation was created by user labeling of the
emails. The image spam (and image ham even more) tackles privacy issues. We overcame it by publishing
extracted feature vectors with associated classes (instead of images itself). This data does not violate privacy
issues. We have published Email.cz image spam dataset v1 via the AcademicTorrents platform and propose a
system, which achieves up to 96% F1-score with presented model architecture on this novel dataset. Providing
our dataset to the community may help others with solving similar tasks.
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