Optical Character Recognition (OCR) is a widely used tool to extract text
from scanned documents. Today, the state-of-the-art is achieved by exploiting
deep neural networks. However, the cost of this performance is paid at the
price of system vulnerability. For instance, in backdoor attacks, attackers
compromise the training phase by inserting a backdoor in the victim's model
that will be activated at testing time by specific patterns while leaving the
overall model performance intact. This work proposes a backdoor attack for OCR
resulting in the injection of non-readable characters from malicious input
images. This simple but effective attack exposes the state-of-the-art OCR
weakness, making the extracted text correct to human eyes but simultaneously
unusable for the NLP application that uses OCR as a preprocessing step.
Experimental results show that the attacked models successfully output
non-readable characters for around 90% of the poisoned instances without
harming their performance for the remaining instances.

提出一种光学字符识别 OCR 后门攻击，通过插入非可读字符的恶意输入图像，使 OCR 模型在某些实例中输出非可读字符，同时不影响其在其他实例中的性能。

隐形威胁：OCR 系统中的后门攻击

Invisible Threats: Backdoor Attack in OCR Systems

Domain divergence plays a significant role in estimating the performance of a
model in new domains. While there is a significant literature on divergence
measures, researchers find it hard to choose an appropriate divergence for a
given NLP application. We address this shortcoming by both surveying the
literature and through an empirical study. We develop a taxonomy of divergence
measures consisting of three classes -- Information-theoretic, Geometric, and
Higher-order measures and identify the relationships between them. Further, to
understand the common use-cases of these measures, we recognise three novel
applications -- 1) Data Selection, 2) Learning Representation, and 3) Decisions
in the Wild -- and use it to organise our literature. From this, we identify
that Information-theoretic measures are prevalent for 1) and 3), and
Higher-order measures are more common for 2). To further help researchers
choose appropriate measures to predict drop in performance -- an important
aspect of Decisions in the Wild, we perform correlation analysis spanning 130
domain adaptation scenarios, 3 varied NLP tasks and 12 divergence measures
identified from our survey. To calculate these divergences, we consider the
current contextual word representations (CWR) and contrast with the older
distributed representations. We find that traditional measures over word
distributions still serve as strong baselines, while higher-order measures with
CWR are effective.

通过文献调研和实证研究，我们开发了一个包含信息论、几何和高阶测量的分类体系，并识别出它们之间的关系，并识别了三种新应用。我们发现，在 130 个域适应场景，3 个不同的 NLP 任务和从文献调查中确定的 12 个分歧措施之间进行的相关分析，具有当前上下文单词表示法的高阶措施是有效的。