Universal Defensive Underpainting Patch: Making Your Text Invisible to
  Optical Character Recognition
JiaCheng Deng Li Dong Jiahao Chen Diqun Yan Rangding Wang Dengpan Ye Lingchen Zhao Jinyu Tian
Optical Character Recognition (OCR) enables automatic text extraction from
scanned or digitized text images, but it also makes it easy to pirate valuable
or sensitive text from these images. Previous methods to prevent OCR piracy by
distorting characters in text images are impractical in real-world scenarios,
as pirates can capture arbitrary portions of the text images, rendering the
defenses ineffective. In this work, we propose a novel and effective defense
mechanism termed the Universal Defensive Underpainting Patch (UDUP) that
modifies the underpainting of text images instead of the characters. UDUP is
created through an iterative optimization process to craft a small, fixed-size
defensive patch that can generate non-overlapping underpainting for text images
of any size. Experimental results show that UDUP effectively defends against
unauthorized OCR under the setting of any screenshot range or complex image
background. It is agnostic to the content, size, colors, and languages of
characters, and is robust to typical image operations such as scaling and
compressing. In addition, the transferability of UDUP is demonstrated by
evading several off-the-shelf OCRs. The code is available at
https://github.com/QRICKDD/UDUP.