  Deep neural networks (DNNs) significantly improved the accuracy of optical
character recognition (OCR) and inspired many important applications.
Unfortunately, OCRs also inherit the vulnerabilities of DNNs under adversarial
examples. Different from colorful vanilla images, text images usually have
clear backgrounds. Adversarial examples generated by most existing adversarial
attacks are unnatural and pollute the background severely. To address this
issue, we propose the Fast Adversarial Watermark Attack (FAWA) against
sequence-based OCR models in the white-box manner. By disguising the
perturbations as watermarks, we can make the resulting adversarial images
appear natural to human eyes and achieve a perfect attack success rate. FAWA
works with either gradient-based or optimization-based perturbation generation.
In both letter-level and word-level attacks, our experiments show that in
addition to natural appearance, FAWA achieves a 100% attack success rate with
60% less perturbations and 78% fewer iterations on average. In addition, we
further extend FAWA to support full-color watermarks, other languages, and even
the OCR accuracy-enhancing mechanism.

Computer Vision and Pattern Recognition;Cryptography and Security;Machine Learning