Deep Learning-Based Fake Image Detection Using Transfer Learning: A Systematic Review

Abstract

The rapid advancement of artificial intelligence (AI) has significantly changed the way digital visual content is created, enabling the generation of highly realistic synthetic images and videos. While these technologies support many beneficial applications, they have also facilitated the creation of manipulated visual content, commonly known as deepfakes, which pose serious challenges to information authenticity, public trust, cybersecurity, and digital forensic investigations. As image manipulation techniques continue to evolve through advanced models such as Generative Adversarial Networks (GANs) and diffusion-based frameworks, conventional detection methods relying on handcrafted features have become increasingly inadequate. In response, deep learning approaches integrated with transfer learning have emerged as effective solutions due to their ability to leverage pre-trained models for extracting robust and discriminative features, even when limited training data are available. This review presents a comprehensive analysis of recent deep learning and transfer learning techniques for fake image detection. It examines widely adopted convolutional neural network (CNN) architectures, benchmark datasets, evaluation metrics, and current research developments. Furthermore, the paper provides a comparative assessment of existing methods by highlighting their strengths, limitations, and performance characteristics. Finally, it identifies major research challenges and outlines future directions for developing robust, scalable, and generalizable fake image detection systems capable of addressing the growing threats posed by AI-generated visual content in cyberspace