Digital Image Forgery Detection refers to the process of identifying whether a digital image has been manipulated or altered in any way, with the intention of deceiving or misleading the viewer. Digital image forgery detection has become an increasingly important research area due to the widespread availability of powerful image editing tools and the potential misuse of manipulated images. In this survey paper, we present a comprehensive comparative analysis of various digital image forgery detection methods. Our objective is to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the existing approaches and their effectiveness in detecting different types of image forgeries. The survey begins by outlining the different types of digital image forgeries, including copy-move, splicing, retouching, and more. We then proceed to review and compare a range of forgery detection methods proposed in the literature. These methods include traditional techniques based on handcrafted features, as well as more recent approaches that leverage advanced machine learning and deep learning algorithms. For each method, we discuss its underlying principles, key components, and specific algorithms utilized. We also analyse the strengths and limitations of each method, considering factors such as detection accuracy, robustness to image transformations, computational efficiency, and the ability to handle complex forgery scenarios. Additionally, we examine the evaluation metrics employed to assess the performance of these methods, including detection accuracy, localization accuracy, processing time, and other relevant metrics. Through our comparative analysis, we identify common trends, challenges, and advancements in the field of digital image forgery detection. We highlight the trade-offs between different methods and provide insights into their suitability for specific forgery types and real-world applications. Furthermore, we discuss the limitations and potential areas for improvement in the existing techniques, aiming to guide future research efforts in developing more effective and robust forgery detection methods.