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.

Introduction

The rapid advancement of artificial intelligence, particularly deep learning and generative models, has enabled the creation of highly realistic synthetic images that are often indistinguishable from real photographs. Technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models have transformed fields like entertainment, digital art, and data augmentation. However, these technologies have also facilitated the creation of deepfakes and fake images that threaten digital trust, privacy, and information integrity through misinformation, identity theft, fraud, and reputational damage. As a result, accurate and reliable fake image detection has become an important research area.

Deepfake images are commonly generated using GANs, where a generator creates synthetic images while a discriminator attempts to distinguish them from real ones. Advanced GAN models such as StyleGAN and ProGAN produce high-resolution, photorealistic images that are increasingly difficult to detect using traditional image forensic techniques. Conventional detection methods rely on handcrafted features and statistical inconsistencies but struggle against sophisticated deep learning-generated images. Consequently, researchers have shifted toward deep learning-based detection systems that automatically learn discriminative features from images and achieve significantly higher detection accuracy.

Transfer learning has become a key technique in fake image detection because it allows pretrained convolutional neural networks (CNNs), originally trained on large datasets such as ImageNet, to be adapted for forensic tasks. Models including ResNet, VGGNet, Inception, DenseNet, EfficientNet, Xception, and MobileNet have demonstrated strong performance in distinguishing real and fake images. Transfer learning reduces training time, performs well with limited labeled data, and provides better generalization to unseen fake image generation methods. Nevertheless, the rapid evolution of generative models means that detection systems must continually adapt to remain effective.

The literature consistently demonstrates the effectiveness of deep learning and transfer learning for fake image detection. Studies have reported detection accuracies ranging from approximately 93% to 99% using architectures such as VGG19, DenseNet, CSWin Transformer, ensemble CNNs, and hybrid CNN-Transformer models. Recent research also explores hybrid approaches that combine spatial and frequency-domain features, transformer networks, ensemble learning, and explainable AI to improve robustness against adversarial attacks and emerging deepfake techniques. Despite these advances, challenges remain in cross-domain generalization, computational efficiency, dataset diversity, and real-world deployment.

The paper also reviews the major fake image generation techniques. GANs remain the most widely used framework for producing photorealistic images through adversarial training. Autoencoder-based models are primarily used for face swapping and identity manipulation, while diffusion models generate highly realistic images by progressively removing noise from random inputs. These increasingly sophisticated generative techniques make automated detection progressively more difficult.

Transfer learning offers several important advantages in fake image detection. It significantly reduces training time by reusing pretrained visual features, improves detection accuracy when labeled forensic data are limited, and enhances the model's ability to generalize to previously unseen manipulation techniques. Different CNN architectures provide unique strengths: VGGNet effectively captures fine texture patterns, ResNet improves deep feature learning through residual connections, Inception extracts multi-scale features, MobileNet enables efficient real-time deployment on mobile devices, while EfficientNet and DenseNet balance accuracy, computational efficiency, and robustness.

Reliable benchmark datasets are essential for developing and evaluating fake image detection systems. Commonly used datasets include CelebA-HQ, FFHQ, FaceForensics++, DeepFake Detection Challenge (DFDC), and GANFake, which provide large collections of real and manipulated images for training and testing. These datasets enable researchers to compare different detection models and evaluate their ability to generalize across various image manipulations and generation techniques.

Conclusion

The blistering development of deep learning has altered the situation in the sphere of digital media, allowing generating extremely realistic fake images and deepfakes with the help of GANs, autoencoders, and diffusion models. These attacks have a serious risk on privacy, security, and information integrity, as well as, there is an urgent necessity of powerful detecting systems. Transfer learning has become an effective methodology in this field, where the use of discriminative features can be obtained efficiently using a pre-trained convolutional neural network even when using small labeled datasets. The tuning of models, including ResNet, EfficientNet, DenseNet, and MobileNet, has enabled researchers to obtain high-performance detection on benchmark datasets at low computational cost and training time. Comparative studies have shown that the choice of a model should find a compromise between accuracy, scalability and deployment limits, and that hybrid and ensemble models can also add extra robustness. Although these advances exist, there are still issues such as the fast development of generative models, dataset bias, high computational costs, adversarial attacks, and the lack of studies on the real implementation. The directions to follow in the future are in multi-modal detection frameworks, CNNTransformer, lightweight CNN-based architectures designed to support mobile devices, continuity-based learning strategies, and explainable AI to build transparency and trust. In general, the concept of fake image detection with the help of deep learning and transfer learning is an important aspect of the digital forensics field that provides effective mechanisms to prevent the synthetic media, yet the necessity to continue research and ensure flexibility in the response to more specific image creation methods remains.

References

The blistering development of deep learning has altered the situation in the sphere of digital media, allowing generating extremely realistic fake images and deepfakes with the help of GANs, autoencoders, and diffusion models. These attacks have a serious risk on privacy, security, and information integrity, as well as, there is an urgent necessity of powerful detecting systems. Transfer learning has become an effective methodology in this field, where the use of discriminative features can be obtained efficiently using a pre-trained convolutional neural network even when using small labeled datasets. The tuning of models, including ResNet, EfficientNet, DenseNet, and MobileNet, has enabled researchers to obtain high-performance detection on benchmark datasets at low computational cost and training time. Comparative studies have shown that the choice of a model should find a compromise between accuracy, scalability and deployment limits, and that hybrid and ensemble models can also add extra robustness. Although these advances exist, there are still issues such as the fast development of generative models, dataset bias, high computational costs, adversarial attacks, and the lack of studies on the real implementation. The directions to follow in the future are in multi-modal detection frameworks, CNNTransformer, lightweight CNN-based architectures designed to support mobile devices, continuity-based learning strategies, and explainable AI to build transparency and trust. 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Copyright © 2026 Nisha Parveen, Anjali Saxena Saxena. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.