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6 CONCLUSION
We presented in this paper an steganalysis attack based
on nine popular machine learning algorithms of the
newly proposed text embedding method of Aziz et al.
(Aziz et al., 2022). We found that the accuracy val-
ues of detecting embedded content using our machine
learning classifiers was surprisingly low (0.553, 0.584
and 0.555), generally resulting in a ROC curve close
to random guess. This initial study indicates that the
new method proposed in (Aziz et al., 2022) so far can
withstand standard machine learning-based attacks.
We plan to continue applying other attack meth-
ods, in particular, other statistical attack methods such
as the 𝜒
2
attack (Pearson, 1900; Plackett, 1983), to
determine if the method can still withstand security
attacks. We also plan to test the embedding capacity
of the (Aziz et al., 2022) to determine its efficiency
in embedding large sized hidden content, and whether
size is a factor in undermining the security of this em-
bedding method.
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