Signature forgery detection using deep and machine learning

Signature forgery detection remains a challenge in the field of biometric security. The goal is to develop automated detection systems capable of distinguishing genuine signatures from forged ones with high accuracy. Traditional signature verification methods, which rely on human judgment, are not only error-prone but also lack the speed and scalability required for modern security requirements. In this paper, we present a hybrid approach that combines the feature extraction capabilities of pre-trained deep learning models with the classification accuracy of traditional machine learning algorithms. The deep learning models used in our approach include state-of-the-art convolutional neural networks, which have demonstrated high performance in image recognition tasks. These models extract highly informative features from signature images, capturing both the overall structure and subtle, differentiated variations. After feature extraction, traditional machine learning classifiers are used to classify signatures as genuine or forged. This hybrid method utilizes the strengths of both paradigms, where deep learning models excel at feature extraction, while machine learning classifiers achieve accurate classification. Experimental results on the CEDAR dataset demonstrate that our best-performing hybrid model, VGG16 + SVM, achieves an accuracy of 95.5%, outperforming standalone deep learning models while maintaining computational efficiency. The proposed approach also attains an F1-score of 95.4%, with precision and recall values of 94.8% and 96.2%, respectively. Comparisons with traditional feature-based classifiers further highlight the superiority of our hybrid method in offline signature verification process. Additionally, our method significantly reduces computational costs compared to deep learning-only approaches, making it suitable for real-time applications.