Automated Signature Authentication Using Deep Convolutional Neural Network
Vikaas Karthik K*1, Sandhya S2
1Vikaas Karthik K, Department of Computer Science and Engineering, R V College of Engineering, Bangalore (Karnataka), India.
2Dr. Sandhya S, Department of Computer Science and Engineering, R V College of Engineering, Bangalore (Karnataka), India.
Manuscript received on 03 September 2026 | First Revised Manuscript received on 09 September 2026 | Second Revised Manuscript received on 06 October 2026 | Manuscript Accepted on 15 October 2026 | Manuscript published on 30 October 2026 | PP: 1-5 | Volume-14 Issue-11, October 2026 | Retrieval Number: 100.1/ijese.K261613111025 | DOI: 10.35940/ijese.K2616.13111025
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Verifying the authenticity of handwritten signatures remains a vital challenge in biometric security systems, especially in financial, legal, and administrative domains. This work presents a deep learning–based framework for automated offline signature authentication using transfer learning with MobileNetV2. The system distinguishes real from forged signatures through convolutional feature extraction and classification. Leveraging MobileNetV2’s lightweight architecture and ImageNet pretraining, the model is fine-tuned on a labelled signature dataset using data augmentation and adaptive training. The pipeline consists of four core modules: Data Preprocessing, CNN Feature Extraction, Transfer Learning Fine-Tuning, and Real-Time Signature Prediction. Evaluation on a real-world dataset achieves 96.46% accuracy, as shown in the confusion matrix. This framework provides a reliable, scalable solution for signature verification in practical security applications, especially where computational efficiency and high precision are essential.
Keywords: Signature Verification, Deep Learning, Transfer Learning, MobileNetV2, Convolutional Neural Network, Biometric Authentication, Forgery Detection.
Scope of the Article: Computer Science and Engineering