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@article{165278,
        author = {Saadhvi Hosmane and Punyashree M and Aditi Ladia and Anirudha Malpani},
        title = {Image Tampering Detection with ELA and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {788-794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165278},
        abstract = {Photographs are the foremost powerful and trustworthy media of expression. At present, digital images not only give forged information but also work as agents of secret communication. Users and editing professionals manipulate digital images with various objectives. In fact, images are often considered as evidence of a fact or reality, therefore, fake news or any form of publication that uses images that have been manipulated in such a way as to have the capability and greater potential for misleading. To detect falsification of the image, image data is required in large quantities multiple, and a model that can process every pixel in picture. In addition, efficiency and flexibility in data training is also needed to support its use in everyday life. Deep learning concepts like Convolutional Neural Network (CNN) with Error Level Analysis is the perfect solution for this problem.},
        keywords = {Image forgery Detection, Convolutional Neural Network, Error Level Analysis, Deep Learning.},
        month = {},
        }