Jul 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
This review presents a comprehensive analysis of recent deep learning and transfer learning techniques for fake image detection, examining widely adopted convolutional neural network architectures, benchmark datasets, evaluation metrics, and current research developments.
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
Experimental results demonstrate that the proposed approach effectively identifies deepfake images with high accuracy, making it suitable for applications in digital forensics, media verification, and cybersecurity.
J. Kollu, Mortha Pavan, Putta Vardhan et al.· International Journal of Inn...· 0 citations
The rapid growth of artificial intelligence has led to the development of advanced technologies capable of generating highly realistic fake images and videos, commonly known as deepfakes. Although these technologies have useful applications in entertainment, education, and digital media, they can also be misused for spreading misinformation, identity theft, fraud, and other cybercrimes. This paper presents an AI-based fake image and video detection system using deep learning models such as InceptionV3, EfficientNet, and a Hybrid model. The proposed system preprocesses input images and video frames through resizing, normalization, and data augmentation to improve model performance. The trained models extract meaningful visual features and classify the uploaded media as real or fake. The Hybrid model combines the strengths of InceptionV3 and EfficientNet to achieve improved feature extraction and higher classification accuracy. The performance of the proposed system is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Hybrid model outperforms the individual models in detecting manipulated media. The proposed approach provides an efficient, accurate, and reliable solution for identifying AI-generated content and helps improve the authenticity, security, and trustworthiness of digital media across various online platforms.
Vidyashree H R and Dr. Manjunath B· International Journal of Adv...· 0 citations
Background: Deepfake technology is a major social concern due to the rapid development of Artificial Intelligence (AI), especially in machine learning (ML) and deep learning (DL). Deepfakes are artificially modified videos and images that effectively change a person’s facial features or expressions to misrepresent reality. These videos and images, often unnoticed by casual observers, present significant ethical, political, and social implications, as they can spread misinformation, damage reputations, and influence public perception. This study is an attempt to detect artificially modified videos by analyzing each frame using DL. We use ResNet-50 architecture, a well-known Convolutional Neural Network (CNN) model, to identify tampered videos. Methods: The system is trained using the Celebrity Deep Fake dataset, which includes numerous original and fake video samples. The model assesses whether each frame is original or tampered with after the videos have been split into frames. The system is tested and evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Results: The model achieved 82.33% accuracy, 76.70% precision, 89.15% recall, and an F1-score of 82.46%. These results indicate that deepfake videos were correctly detected and that the model was efficient at identifying most real deepfake instances. In addition, the F1-score of 82.46% is further evidence of the model’s stability, as it ensures both high accuracy and coherence across numerous cases. Conclusions: The findings indicate that the proposed model works effectively compared to existing deepfake methods. In the future, we intend to use a richer dataset with more resources, which may further enhance the accuracy of the model.
Iftikhar Alam, Malik Ahsan Kamran, Ehaab Ullah· The International Journal of...· 0 citations
The rapid growth of social media has made image sharing a common part of daily communication, but it has also increased the spread of manipulated and misleading images. Fake images can influence public opinion, damage reputations, and contribute to the circulation of false information. This study presents a Hybrid Deep Learning Framework for Social Media Image Forgery Detection, designed to automatically identify forged and authentic images using deep learning techniques. The proposed framework employs a Convolutional Neural Network (CNN) to learn visual patterns and distinguish manipulated images from genuine ones. Before training, the image dataset undergoes preprocessing steps such as resizing, normalization, and data augmentation to improve model performance and generalization. The trained model is evaluated using standard performance measures, including accuracy, precision, recall, and F1-score, to ensure reliable classification. A user-friendly web application is also developed to allow users to upload images and receive instant authenticity predictions. The proposed framework provides an effective solution for detecting fake images on social media, supporting digital content verification, reducing misinformation, and helping users make informed decisions regarding the credibility of shared visual information.
Shankarpally Bhargavi, G.Rajini· International Journal of Eng...· 0 citations
The evolution of sophisticated generative artificial intelligence has led to the rapid development of very realistic manipulated images and videos, posing substantial risks for digital trust, cyber security, and multimedia authenticity. Advanced Deepfake generation technologies result in the creation of believable forgery media which become hard to differentiate from authentic media; this leads to misinformation, identity spoofing, and digital scams. Therefore, precise and effective authentication of multimedia becomes an imperative requirement for digital forensics investigation and online content authentication. This research paper presents a ResNet-powered deep feature learning approach for detecting Deepfake images and videos. The suggested approach normalizes and resizes images, while videos are decomposed into frames for thorough spatial-temporal analysis. The hybrid convolutional neural network model, which is built on top of ResNet architecture, extracts discriminative features that represent subtle manipulation traces, face texture inconsistency, and structural abnormalities. In addition, inverted residual blocks and linear bottlenecks are used to increase computational efficiency. The deep learning-based feature extraction process is then followed by the classification stage to distinguish between genuine multimedia content and forged multimedia content. From experimental studies, it can be shown that the proposed framework helps to enhance the detection rate, robustness toward new Deepfake methods, and enables real-time implementation. The research provides an effective solution for multimedia authentication applications.
Bella Inba Suganthi V, S. Jose· International Journal of Sci...· 0 citations
The research methodology involved a systematic literature review using the Scopus database, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and focusing on recent advancements in attack and defence techniques.
