Anti-Counterfeit Handwritten Signature via DCGAN with SGPD Network

Abstract

In recent years, the growth of machine learning makes the computer can learn many things by using artificial intelligence. One method that is feared nowadays is the computer's capability to imitate something. This capability is called deep-fake. Deep-fake is the capability of the computer to imitate human characteristics such as voice, images, and video through artificial intelligence. Deep-fake is used to combine put the consisted image and video to another source of images and video using machine learning which is known as a generative adversarial network. With these capabilities, deep-fake is already used to make a counterfeit video, signature, voice signature, and much fake news. This paper is about to combine the capabilities of deep learning and the Generative Adversarial Network (GAN) to deal with detecting the fraud in the handwritten signature. We will focus on several types of ways to sign with the characters. The system will recommend if the hand signature of the user is fake or genuine. This is under the capabilities of GAN to synthesize the signature, it can make the computer automatically generate hand signature by using a machine. Many researchers called this capability is deep-fake. This research aims to learn the hand signature to do fraud detection. We propose an architecture to build the anti-counterfeiting hand signature which is utilized deep learning with a self-growing probabilistic method.