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@article{170121,
        author = {Mandar Borkar and Tanishka Pitale and Mohini Kate and Anil Walke and Nikita Khawase},
        title = {A Hybrid Approach to Image Forgery Detection: Leveraging ELA and CNNs for Enhanced Accuracy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3412-3416},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170121},
        abstract = {This paper presents a robust hybrid approach to image forgery detection, combining Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs). ELA is utilized to identify discrepancies in image compression, while CNNs are employed for automated classification of tampered versus authentic images. Extensive hyperparameter tuning and data augmentation techniques were applied to achieve high classification accuracy. With a carefully crafted CNN architecture, the model achieved an accuracy of 94%, demonstrating significant improvements over traditional methods. Furthermore, the model’s robustness was tested across various image conditions, and a comprehensive error analysis was provided. This approach outperforms other state-of-the-art methods, particularly in handling subtle tampering cases.},
        keywords = {Image Forgery, Deep Learning, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Image Forensics.},
        month = {November},
        }