The detection of forged signatures is a critical challenge in various fields, including banking, legal documentation, and identity verification. Traditional methods for signature verification rely on handcrafted features and machine learning models, which often struggle to generalize across varying handwriting styles and sophisticated forgeries. In recent years, deep learning techniques have emerged as powerful tools for tackling this problem, leveraging large datasets and automated feature extraction to enhance accuracy. In this literature survey paper, we have studied and analyzed various research papers on fake signature detection, focusing on the accuracy of different deep learning techniques. The primary models reviewed include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). We evaluated the performance of these methods based on their reported accuracy on benchmark datasets, highlighting the strengths and limitations of each approach. Additionally, we discussed challenges such as dataset scarcity and the difficulty of generalizing models to detect different types of forgeries. Our analysis provides insights into the effectiveness of these methods and suggests potential directions for future research in improving signature verification systems.