论文标题
对基于眼的用户建模的分类器对分类器的对抗性攻击
Adversarial Attacks on Classifiers for Eye-based User Modelling
论文作者
论文摘要
不断增长的工作已经证明了用户建模的眼动中可用的丰富信息内容，例如用于预测用户的活动，认知过程甚至人格特质。我们表明，用于基于眼睛的用户建模的最新分类器非常容易受到对抗性示例的攻击：凝视输入中的小型人为扰动可以极大地改变分类器的预测。我们使用快速梯度符号方法（FGSM）生成这些对抗性示例，该方法线性化梯度以找到合适的扰动。在基于眼睛的文档类型识别的示例任务中，我们研究了不同对抗性攻击方案的成功：有或没有有关分类器梯度（白框与黑色框）以及不针对特定阶级的攻击的知识，我们还证明了通过将对抗性示例添加到对抗性示例中，通过将对抗性示例添加到分类者的培训数据中。
An ever-growing body of work has demonstrated the rich information content available in eye movements for user modelling, e.g. for predicting users' activities, cognitive processes, or even personality traits. We show that state-of-the-art classifiers for eye-based user modelling are highly vulnerable to adversarial examples: small artificial perturbations in gaze input that can dramatically change a classifier's predictions. We generate these adversarial examples using the Fast Gradient Sign Method (FGSM) that linearises the gradient to find suitable perturbations. On the sample task of eye-based document type recognition we study the success of different adversarial attack scenarios: with and without knowledge about classifier gradients (white-box vs. black-box) as well as with and without targeting the attack to a specific class, In addition, we demonstrate the feasibility of defending against adversarial attacks by adding adversarial examples to a classifier's training data.
