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| Noise Analysis for Text-based Spam Images |
| Peng Li, Hanbing Yan, Gang Cui, Yuejin Du |
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Abstract Traditional spam filters are facing more and more challenges with
the rapid growth of image-based spam. Previous works have leveraged
OCR techniques and text classifiers for image spam detection, which
are time consuming and CPU intensive. In addition, OCR can be
easily tricked by noise- and content-obscuring elements added by
spammers. In this paper, we propose a novel approach aimed at
detecting the ``{\it amount}'' and the ``{\it type}'' of noise due
to the use of those techniques against OCR tools. Firstly, we
propose a specific method for text region localization using
steerable filter and morphological processing, which separates
images into text-regions for OCR content extraction and
background-regions for noise analysis. Next, wavelet transform is
used for constructing noise feature image of the background-region,
based on which noise measurement and classification can be
completed. Experimental results show that our method can locate the
text region accurately, and the results of noise analysis can
effectively reflect the noise interference of spam images, which
can be viewed as complementary to the approaches based on OCR tools
for further reducing false positives of the image spam filters.
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