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@article{203654,
        author = {Neha Verma and Prof. Dr. D.N. Goswami and Prof. Dr. Anshu Chaturvedi},
        title = {Deepfake Image Detection: A Comprehensive Review of Techniques, Challenges, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12611-12621},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203654},
        abstract = {In this research, the authors offer an extensive overview of the methods currently developed to detect deepfake images—from traditional machine learning methods to deep learning, hybrid models, and transformer-based approaches. It provides a depth analysis of benchmark datasets, evaluation metrics, and key challenges like generalization, real-time requirements, and adversarial attacks. Moreover, it is focusing on new research trends such as federated learning, explainable AI, lightweight models, and blockchain integration. In the current context of fast-evolving generative AI technologies, the review highlights the importance of systems capable of detecting AI-generated content that are robust, scalable, and privacy-protection, to maintain authenticity and trust in digital media.},
        keywords = {Deepfake detection, Artificial intelligence, Deep learning, GANs, CNNs, Transformer models, Image forgery, Synthetic media, Federated learning, Explainable AI.},
        month = {May},
        }