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@article{154512,
        author = {Saadhvi Hosmane and Punyashree M and Aditi Ladia and Anirudha Malpani and Manjunath S},
        title = {Study on Deep Learning Based Techniques for Image Tamper Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {368-375},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154512},
        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. Scientists and researchers manipulate images for his or her work to urge published; medical images are tampered to misrepresent the patients’ diagnostics, journalists use the trick for creating and giving dramatic effect to their stories, politicians, lawyers, forensic investigators use tampered images to direct the opinion of people, court, or law to their favor then on.  Hence, distinguishing the primary images from faked lots and establishing the authenticity of digital photographs has gained much importance in recent times. The objective of this study is to understand different techniques to detect image tampering using Deep Learning.},
        keywords = {Block-based approach, Copy-Move, CNN, Deep Learning, Image Tampering.},
        month = {},
        }