Abstract
The in-air signature verification system has garnered attention due to its flexibility, friendliness, security, high efficiency, remote accessibility, and contactless usage patterns. However, with the advancement of machine learning, computers can now learn and imitate many things through artificial intelligence. This technology, referred to as “deepfake”, has been used to create fake signatures, raising concerns about the security of the in-air signature verification system. Consequently, research on in-air signature verification technology against deepfakes has become an urgent need. The current challenges are: (1) Most research on in-air signature verification at this stage focuses on human forgery, with a lack of high-performance verification systems for deepfake in-air signatures. (2) Due to the long sequence and small amount of data, there is a lack of good generation methods for in-air signature generation tasks. To address these challenges, this paper proposes an improved in-air feature data model based on the GLNLSTM autoencoder. The signature samples generated by this model are more authentic than the baseline model. Additionally, we introduce an in-air signature verification model based on a two-channel model. The signature semantic feature extraction module of this model uses one-dimensional CNN and bidirectional LSTM to extract dynamic time features. This model achieves the best results in both deepfake verification and artificial forgery signature verification tasks on the SCUT-MMSIG-AIR database.
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Acknowledgements
This study is supported by the Project for Science and Technology of Inner Mongolia Autonomous Region under Grant 2019GG281, the Natural Science Foundation of Inner Mongolia Autonomous Region under Grant 2019ZD14, the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region under Grant NJYT-20-A05, the fund of supporting the reform and development of local universities (Disciplinary Construction) and construction project of “Inner Mongolia Science and Technology Achievement Transfer and Transformation Demonstration Zone, University Collaborative Innovation Base, and University Entrepreneurship Training Base” (Supercomputing Power Project: 21300-231510).
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Yu, H., Wei, H., Wang, Y. (2024). Deepfake In-Air Signature Verification via Two-Channel Model. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14805. Springer, Cham. https://doi.org/10.1007/978-3-031-70536-6_17
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