Signature Forgery Detection
DOI:
https://doi.org/10.52783/jns.v14.3766Keywords:
Signature verification, forgery detection, offline signature verification, online signature verification, feature extraction, machine learning models, convolutional neural networks (CNN), support vector machine (SVM), k-nearest neighbors (KNN), data augmentation, synthetic signatures, feature vector representation, classification algorithms, validation and testing, cross-validation.Abstract
Digital signatures are widely used in recent times by organisations public and private alike. Similar to fingerprints the signatures are legally binding. Another reason why they are being used is that they are easy to handle and store. The digital signatures are utilized mainly in e-commerce websites for delivery authentication to the customers, bank customer procedures and verification, government organisations and various other businesses big and small. Nowadays, the government uses digital signatures for contracts and verifying documents. When there is an advancement in IT, it has its advantages and disadvantages. Signature is one of the important biometric techniques that may be used for manipulating the signature data and using them for malicious purposes. Two efficient machine learning algorithms VGG16 and random forest are implemented in the research attempt to identify a way to mitigate the risk caused by signature forgery. The machine language techniques are being trained and tested to check whether the signatures given are original or fake using a data set
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