Deep Learning Algorithms and Optimizers: Enhancing the Evaluation of Signature Authenticity - Haris Rangkuti, Abdul; Tanuar, Evawaty; Kusuma, Verdiant Jonathan; Athala, Varyl Hasbi (Society of Visual Informatics, 2025)
Given the rapid technological advancements, security has become an essential human need that must be addressed. For example, a signature, which serves as a unique identifier or mark on a document, is vital in verifying and legalizing its contents. This study aims to utilize image processing techniques to identify patterns in signature images. Generally, a signature is a handwritten depiction used to authorize a document, indicating that the signing party acknowledges and agrees to its contents. However, this practice exposes signatures to the risk of forgery by dishonest individuals. Therefore, it is crucial to implement a security system for identity recognition using a biometric system for verification and identification. Verification involves determining whether the signature belongs to a previously identified individual and assessing its authenticity. This study employs deep learning algorithms, enhanced by optimizer methods, to improve accuracy performance in signature recognition for authenticity. Additionally, classification methods such as Linear SVM and RbfSVM are utilized. Several experiments were conducted, with VGG16 paired with the Adam optimizer yielding the highest accuracy of 0.9923. This was closely followed by VGG19 with Adagrad and Xception with RMSprop, achieving an accuracy of 0.9915. The training and validation accuracy processes revealed that the CNN VGG19 and VGG16 models with the Adam optimizer consistently achieved an accuracy exceeding 99%. Based on these experimental findings, the accuracy for detecting genuine and fake signatures can be clearly distinguished with an accuracy of over 99%, demonstrating the validity of this approach.