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@article{191497,
        author = {Dungarwal Riya Sunil and Shimpi Sneha Vijay and Sonavane Srushti Rajesh and Pagar Varuna Santosh and Zambare Dattu Bhausaheb},
        title = {AI Based DeepFake Image Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6934-6936},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191497},
        abstract = {Deepfake technology has advanced rapidly in recent years, enabling the generation of highly realistic synthetic images that are difficult to distinguish from authentic ones. While such technology has beneficial applications, it also poses serious threats including misinformation, identity theft, and digital forgery. This paper presents a Deepfake Image Detection System based on deep learning techniques to identify manipulated or synthetic facial images. The proposed system leverages convolutional neural networks (CNNs) to automatically learn discriminative features from images, such as texture inconsistencies, facial artifacts, and frequency-domain anomalies. Experimental results demonstrate that the proposed approach achieves high detection accuracy on benchmark deepfake datasets, indicating its effectiveness in combating image-based deepfake threats.},
        keywords = {Convolutional Neural Network, Deep Learning, Deepfake Detection, Image Forensics, Synthetic Media.},
        month = {January},
        }