  A major approach for defending against adversarial attacks aims at
controlling only image classifiers to be more resilient, and it does not care
about visual objects, such as pandas and cars, in images. This means that
visual objects themselves cannot take any defensive actions, and they are still
vulnerable to adversarial attacks. In contrast, letters are artificial symbols,
and we can freely control their appearance unless losing their readability. In
other words, we can make the letters more defensive to the attacks. This paper
poses three research questions related to the adversarial vulnerability of
letter images: (1) How defensive are the letters against adversarial attacks?
(2) Can we estimate how defensive a given letter image is before attacks? (3)
Can we control the letter images to be more defensive against adversarial
attacks? For answering the first and second questions, we measure the
defensibility of letters by employing Iterative Fast Gradient Sign Method
(I-FGSM) and then build a deep regression model for estimating the
defensibility of each letter image. We also propose a two-step method based on
a generative adversarial network (GAN) for generating character images with
higher defensibility, which solves the third research question.
