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@article{162588,
        author = {Charan G N and Bhagyashri R. Hanji and Hemanth Kumar V and J S Naga Vishnu Sai and Jeevan S},
        title = {Decoding Deception: Error Level Analysis for Image Forgery Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {438-443},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162588},
        abstract = {Detecting digital image forgery is crucial to safeguard image integrity, especially in an era where manipulation is effortless. Error Level Analysis serves as a valuable tool by decreasing image quality and comparing error levels to identify modifications. This study employs Convolutional Neural Network, a deep learning method to enhance image forgery detection. By introducing Error Level Analysis extraction, the validation accuracy improves by approximately 2.7%, leading to enhanced test accuracy. However, this enhancement comes at the cost of a 5.6% increase in processing time. The research underscores the trade-offs involved in leveraging ELA within a deep learning framework for more effective image authenticity verification.},
        keywords = {Image Forgery Detecting, ELA, CNN, Digital Content Verification, Tampered Images, Flask framework, Image Preprocessing.},
        month = {},
        }