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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Detection Of Image Forgeries Using Ant Colony Optimization (ACO) Methodology |
| Authors Name: | Ms. Bindhu A , Ms. Dhanusha A |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_181223
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| Published Paper Id: | IJRTI2004008 |
| Published In: | Volume 5 Issue 4, April-2020 |
| DOI: | |
| Abstract: | Now-a-days, digital images have acquired the reputation of being an important evidence. However, with the development of imaging technology and the accessibility of powerful affordable image editing tools like Photoshop, it is becoming easier to add, modify or remove important features from an image without leaving any visual traces. Any image manipulation can become a forgery, based upon the context in which it is used. Thus today digital images are losing authenticity and it is becoming difficult to distinguish between authentic and tampered images; which is an essential requirement in various areas like in legal cases, in electronic media, in medical profession, and in research works etc. Copy move is the most common technique used for creating digital image forgeries in which a part of an image is copied and pasted elsewhere in the same image. Due to the technology advancement and availability of lots of sophisticated image editing tools, the digital images are losing authenticity. This has led to the proposal of different detection techniques to check whether the digital images are authentic or forged. Copy move forgery is a special type of forgery technique whose detection has become a widely used research topic under digital image forensics. The proposed system implement the feature extraction using principle component analysis and optimization (ant colony optimization) algorithm to detect the forgery image in JPG images. In optimization approach to classify the features and match the training feature if training and testing features has matching then detect the forgery image in the jpg images. |
| Keywords: | image forgery, copy move detection, digital image editing, ant colony optimization |
| Cite Article: | "Detection Of Image Forgeries Using Ant Colony Optimization (ACO) Methodology", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.5, Issue 4, page no.54 - 58, April-2020, Available :http://www.ijrti.org/papers/IJRTI2004008.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2004008
Registration ID:181223
Published In: Volume 5 Issue 4, April-2020
DOI (Digital Object Identifier):
Page No: 54 - 58 Country: Kanyakumari, Tamilnadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2004008 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2004008 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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