Use of Auxiliary Classifier Generative Adversarial Network in Touchstroke Authentication
Use of Auxiliary Classifier Generative Adversarial Network in Touchstroke Authentication
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DOI：
10.1109/icmla51294.2020.00049
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
D. Deb;Mina Guirguis
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文献类型：
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作者：
D. Deb;Mina Guirguis
随着智能手机的日益普及，通过行为生物识别技术（例如触摸动态）对此类设备进行连续和隐式身份验证成为一种有吸引力的选择，特别是当物理生物识别技术难以使用，或者它们的频繁和连续使用令用户烦恼时。然而，触摸动态很容易受到潜在的安全攻击，例如肩窥攻击、摄像头攻击和污迹攻击。因此，仅依靠从真实触摸中学习的模型来排除真正的冒名顶替者是具有挑战性的。本文提出了一种基于辅助分类器生成对抗网络（AC-GAN）的触摸认证模型。给定训练期间合法用户的触摸笔划数据的一小部分，所提出的 AC-GAN 模型学习生成大量与真实触摸笔划非常接近的合成触摸笔划，模拟冒名顶替者行为，然后使用生成的触摸笔划和真实触摸笔划来区分真实用户和冒名顶替者。所提出的网络在 Touchanalytics 数据集上进行训练，并使用流行的性能指标和损失函数来评估可辨别性。评估结果表明，即使生成模型面临大量有效模拟冒名顶替者行为的合成数据的挑战，也可以以 2% 至 11% 的等错误率实现可比的身份验证精度。 AC-GAN的使用还使生成的样本多样化并稳定了训练。
With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast number of synthetic data that effectively simulates an imposter behavior. The use of AC-GAN also diversifies generated samples and stabilizes training.