Title: Adversarial Attacks on Binary Image Recognition Systems. Abstract: We initiate the study of adversarial attacks on models for binary (i.e. black and white) image classification. Although there has been a great deal of work on attacking models for colored and grayscale images, little is known about attacks on models for binary images. Models trained to classify binary images are used in text recognition applications such as check processing, license plate recognition, invoice processing, and many others. In contrast to colored and grayscale images, the search space of attacks on binary images is extremely restricted and noise cannot be hidden with minor perturbations in each pixel. Thus, the optimization landscape of attacks on binary images introduces new fundamental challenges.

In this paper we introduce a new attack algorithm called Scar, designed to fool classifiers of binary images. We show that Scar significantly outperforms existing L0 attacks applied to the binary setting and use it to demonstrate the vulnerability of real-world text recognition systems. Scar’s strong performance in practice contrasts with hardness results that show the existence of worst-case classifiers for binary images that are robust to large perturbations. In many cases, altering a single pixel is sufficient to trick Tesseract, a popular open-source text recognition system, to misclassify a word as a different word in the English dictionary. We also demonstrate the vulnerability of check recognition by fooling commercial check processing systems used by major US banks for mobile deposits. These systems are substantially harder to fool since they classify both the handwritten amounts in digits and letters, independently. Nevertheless, we generalize Scar to design attacks that fool state-of-the-art check processing systems using unnoticeable perturbations that lead to misclassification of deposit amounts. Consequently, this is a powerful method to perform financial fraud.