Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 44, Issue 10, Pages 6472-6485Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2021.3087619
Keywords
Forgery; Databases; Training; Perturbation methods; Distortion; Deep learning; Data models; Dynamic signature verification and synthesis; Sigma Lognormal; average precision optimization; Sig2Vec
Funding
- National Natural Science Foundation of China [61936003, 61771199]
- Natural Science Foundation of Guangdong Province (GD-NSF) [2017A030312006]
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This paper proposes a deep learning-based dynamic signature verification framework called SynSig2Vec. It addresses the issue of skilled forgery attacks by synthesizing samples using a learning-by-synthesis method and extracting signature representations with the Sig2Vec model. Experimental results show that the SynSig2Vec framework achieves state-of-the-art performance on a dynamic signature database.
Handwritten signature verification is a challenging task because signatures of a writer may be skillfully imitated by a forger. As skilled forgeries are generally difficult to acquire for training, in this paper, we propose a deep learning-based dynamic signature verification framework, SynSig2Vec, to address the skilled forgery attack without training with any skilled forgeries. Specifically, SynSig2Vec consists of a novel learning-by-synthesis method for training and a 1D convolutional neural network model, called Sig2Vec, for signature representation extraction. The learning-by-synthesis method first applies the Sigma Lognormal model to synthesize signatures with different distortion levels for genuine template signatures, and then learns to rank these synthesized samples in a learnable representation space based on average precision optimization. The representation space is achieved by the proposed Sig2Vec model, which is designed to extract fixed-length representations from dynamic signatures of arbitrary lengths. Through this training method, the Sig2Vec model can extract extremely effective signature representations for verification. Our SynSig2Vec framework requires only genuine signatures for training, yet achieves state-of-the-art performance on the largest dynamic signature database to date, DeepSignDB, in both skilled forgery and random forgery scenarios. Source codes of SynSig2Vec will be available at https://github.com/LaiSongxuan/SynSig2Vec.
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