论文标题
具有对抗水印的攻击光学识别（OCR）系统
Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks
论文作者
论文摘要
光学特征识别（OCR）被广泛应用于用于关键预处理工具的实际应用中。在OCR中采用深神经网络（DNN）会导致针对对抗性例子的脆弱性，这些示例旨在误导威胁模型的输出。与香草彩色图像不同，印刷文本的图像通常具有清晰的背景。但是，大多数现有的对抗攻击产生的对抗例子是不自然的，并且严重污染了背景。为了解决这个问题，我们提出了一种水印攻击方法，以产生伪装并逃避人眼检测的自然失真。实验结果表明，水印攻击可以产生一组带有水印的自然对抗例子，并在不同的攻击情况下获得与最先进方法相似的攻击性能。
Optical character recognition (OCR) is widely applied in real applications serving as a key preprocessing tool. The adoption of deep neural network (DNN) in OCR results in the vulnerability against adversarial examples which are crafted to mislead the output of the threat model. Different from vanilla colorful images, images of printed text have clear backgrounds usually. However, adversarial examples generated by most of the existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose a watermark attack method to produce natural distortion that is in the disguise of watermarks and evade human eyes' detection. Experimental results show that watermark attacks can yield a set of natural adversarial examples attached with watermarks and attain similar attack performance to the state-of-the-art methods in different attack scenarios.
