Study on Deep Learning Based Techniques for Image Tamper Detection

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Study on Deep Learning Based Techniques for Image Tamper Detection
Author(s):
Saadhvi Hosmane, Punyashree M, Aditi Ladia, Anirudha Malpani, Manjunath S
Keywords:
Block-based approach, Copy-Move, CNN, Deep Learning, Image Tampering.
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.
Article Details
Unique Paper ID: 154512 Publication Volume & Issue: Volume 8, Issue 11 Page(s): 368 - 375
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