  Adversarial machine learning is an emerging area showing the vulnerability of
deep learning models. Exploring attack methods to challenge state of the art
artificial intelligence (A.I.) models is an area of critical concern. The
reliability and robustness of such A.I. models are one of the major concerns
with an increasing number of effective adversarial attack methods.
Classification tasks are a major vulnerable area for adversarial attacks. The
majority of attack strategies are developed for colored or gray-scaled images.
Consequently, adversarial attacks on binary image recognition systems have not
been sufficiently studied. Binary images are simple two possible pixel-valued
signals with a single channel. The simplicity of binary images has a
significant advantage compared to colored and gray scaled images, namely
computation efficiency. Moreover, most optical character recognition systems
(O.C.R.s), such as handwritten character recognition, plate number
identification, and bank check recognition systems, use binary images or
binarization in their processing steps. In this paper, we propose a simple yet
efficient attack method, Efficient Combinatorial Black-box Adversarial Attack,
on binary image classifiers. We validate the efficiency of the attack technique
on two different data sets and three classification networks, demonstrating its
performance. Furthermore, we compare our proposed method with state-of-the-art
methods regarding advantages and disadvantages as well as applicability.
