THE SCIENCE OF SIGNATURE DETECTION: UNDERSTANDING THE TECHNIQUES (PART-I)

        Signatures were being used in the world for nearly a thousand years. It is written in many distinct styles; some might be letters from various languages. It is regarded as confirmation or consent of a person’s agreement with another person. This could be a sales agreement or a contract agreement or any aspect of a person’s identity that may be expressed in a few short letters. The use of signatures has increased along with trade and market expansion. After the industry’s digitalization, many conventional practices, including signatures, were turned into digital representations. This has assisted in raising security standards, but as cybercriminals get more sophisticated daily, crime has also increased. In addition to determining the authenticity of this digitally stored information, it has become increasingly difficult for enterprises to protect the digital information of their customers. Thus verifying the legality of digital signatures has become crucial in the current environment.

Genuine Signature Image

        Fake signature detection using machine learning involves training a model to differentiate between genuine and fake signatures. The process typically involves collecting a dataset of genuine signatures and forged signatures, and then using this data to train a machine learning algorithm. The algorithm is usually designed to extract features from the signature images, such as the stroke width, curvature, and pen pressure, which can help distinguish between real and fake signatures. These features are then used as input to the machine learning model.

Comparative Images(Forged vs Real) 

        The model is trained to learn the patterns and characteristics of genuine signatures, as well as the differences between genuine signatures and forgeries. Once trained, the model can then be used to predict whether a given signature is genuine or fake. Some popular machine learning techniques used in fake signature detection include support vector machines (SVMs), decision trees, and neural networks. The performance of these models depends on the quality and quantity of the training data, as well as the choice of features and model parameters.

Here are some research papers that discuss fake signature detection using machine learning:

  • “Automatic signature verification using hybrid neural network classifier” by A. Arivazhagan and V. Vaidehi. This paper proposes a hybrid neural network classifier for signature verification, which combines a multilayer perceptron neural network and a radial basis function neural network.
  • “Dynamic signature verification using machine learning” by H. Guo and S. L. Phung. This paper presents a method for dynamic signature verification using machine learning, which involves capturing the temporal information of a signature using a digital pen.
  • “Online signature verification using convolutional neural networks” by A. Madhavan and M. Ramaswamy. This paper proposes an online signature verification system based on convolutional neural networks, which extracts features from the signature images using convolutional layers.
  • “Signature verification using artificial neural networks” by S. S. S. Sowmya and S. K. Deepa. This paper presents an artificial neural network-based approach for signature verification, which uses features such as stroke width, direction, and length.
  • “Forgery detection in signature verification using hybrid feature extraction and machine learning” by A. R. Al-Ali and M. J. Hossain. This paper proposes a hybrid feature extraction method for signature verification, which combines handcrafted features such as pressure and curvature with features extracted using a deep convolutional neural network.

        These are just a few examples of the many research papers that have been published on fake signature detection using machine learning.

        Now I will write more about the this project in next blog, so wait till next. And please share your thoughts on this.

        Thank you!!!



Triveni Suresh Gaidhane

B.Tech(Electronics and Telecommunication Engineering)

MIT Academy of Engineering, Pune.

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