Abstract
Handwriting movements can be leveraged as a unique form of behavioral biometrics, to verify whether a real user is operating a device or application. This task can be framed as a reverse Turing test in which a computer has to detect if an input instance has been generated by a human or artificially. To tackle this task, we study ten public datasets of handwritten symbols (isolated characters, digits, gestures, pointing traces, and signatures) that are artificially reproduced using seven different synthesizers, including, among others, the Kinematic Theory (Sigma h model), generative adversarial networks, Transformers, and Diffusion models. We train a shallow recurrent neural network that achieves excellent performance (98.3 percent Area Under the ROC Curve (AUC) score and 1.4 percent equal error rate on average across all synthesizers and datasets) using nonfeaturized trajectory data as input. In few-shot settings, we show that our classifier achieves such an excellent performance when trained on just 10 percent of the data, as evaluated on the remaining 90% of the data as a test set. We further challenge our classifier in out-of-domain settings, and observe very competitive results as well. Our work has implications for computerized systems that need to verify human presence, and adds an additional layer of security to keep attackers at bay.
Abstract (translated)
手写动作可以被用作一种独特的行为生物识别方式，以验证实际用户是否在操作设备或应用程序。这一任务可以被视为一个反向图灵测试，在这种测试中，计算机需要检测输入实例是由真人生成的还是人工合成的。为了解决这个问题，我们研究了十个公开的手写符号数据集（包括孤立字符、数字、手势、指点轨迹和签名），这些数据集使用七种不同的生成器进行了人工再现，其中包括动学理论（Sigma h 模型）、生成对抗网络、Transformer 和扩散模型等。 我们训练了一个浅层循环神经网络，在不进行特征化处理的情况下以轨迹数据为输入，该网络在所有合成器和数据集中实现了卓越的性能（98.3% 的接收者操作特性曲线（ROC）面积得分和1.4% 的平均错误接受率）。此外，我们在少量样本设置下展示了当训练集仅为总数据量的10%，而剩余90%的数据作为测试集时，我们的分类器也能实现如此卓越的表现。我们进一步在跨域场景中挑战了这一分类器，并观察到了非常具有竞争力的结果。 这项工作对需要验证人类存在的计算机系统有着重要的影响，并为防止攻击者侵入添加了一层额外的安全保障。
URL
https://arxiv.org/abs/2601.11700
