Towards generalized morphing attack detection by learning residuals

作者：

Highlights：

• Presents a new approach for Generalizable Morphing Attack Detection by learning residuals from Encoder-Decoder network.

• Proposes to explore complementary color spaces for learning morphing cues in an end-to-end manner.

• Extensive experiments on 5 different databases with 2 landmark-based & 3 Generative Adversarial Network (GAN) based morphing.

• Detailed explainability analysis of proposed approach using three different Class Activation Maps (CAM) for MAD.

摘要

•Presents a new approach for Generalizable Morphing Attack Detection by learning residuals from Encoder-Decoder network.•Proposes to explore complementary color spaces for learning morphing cues in an end-to-end manner.•Extensive experiments on 5 different databases with 2 landmark-based & 3 Generative Adversarial Network (GAN) based morphing.•Detailed explainability analysis of proposed approach using three different Class Activation Maps (CAM) for MAD.

论文关键词：Morphing attacks,Morphing attack detection,Face recognition,Vulnerability of biometric systems

论文评审过程：Received 29 December 2021, Revised 9 August 2022, Accepted 11 August 2022, Available online 28 August 2022, Version of Record 19 September 2022.

论文官网地址：https://doi.org/10.1016/j.imavis.2022.104535