Vulnerability of Appearance-based Gaze Estimation

• URL: http://arxiv.org/abs/2103.13134v1
• Date: Wed, 24 Mar 2021 12:19:59 GMT
• Title: Vulnerability of Appearance-based Gaze Estimation
• Authors: Mingjie Xu, Haofei Wang, Yunfei Liu, Feng Lu
• Abstract summary: Appearance-based gaze estimation has achieved significant improvement by using deep learning. In this paper, we investigate the vulnerability of appearance-based gaze estimation. We show that the CA-Net shows superior performance against attack among the four popular appearance-based gaze estimation networks.
• Score: 16.0559382707645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Appearance-based gaze estimation has achieved significant improvement by using deep learning. However, many deep learning-based methods suffer from the vulnerability property, i.e., perturbing the raw image using noise confuses the gaze estimation models. Although the perturbed image visually looks similar to the original image, the gaze estimation models output the wrong gaze direction. In this paper, we investigate the vulnerability of appearance-based gaze estimation. To our knowledge, this is the first time that the vulnerability of gaze estimation to be found. We systematically characterized the vulnerability property from multiple aspects, the pixel-based adversarial attack, the patch-based adversarial attack and the defense strategy. Our experimental results demonstrate that the CA-Net shows superior performance against attack among the four popular appearance-based gaze estimation networks, Full-Face, Gaze-Net, CA-Net and RT-GENE. This study draws the attention of researchers in the appearance-based gaze estimation community to defense from adversarial attacks.

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