Signature Forgery Detection

The usage of advanced signature verification technologies is required because of the growing dependence on digital transactions and authentication technology. This survey looks at the current state of dynamic signature representation techniques, with a focus on learning without forgeries. The efficacy of enhancing the security of signature-based authentication systems through the combination of 1D CNNs and the novel signature embedding approach Synsig2Vec is assessed. The survey's first section addresses the dangers of forgery attacks and the weaknesses of employing traditional signature verification methods. It then explores the state- of-the-art Synsig2Vec methodology, which provides a more thorough representation by capturing the dynamic characteristics of signatures. By adding 1D CNN, the feature extraction procedure is further enhanced and the model's accuracy in differentiating real signatures from fakes is increased.

Keywords:- Signature Verification, Forgery Detection, Synsig2Vec, 1D CNN, Dynamic Signature Representation, Authentication Systems.