Review Paper on Forgery Image Detection and Classification using Machine Learning
Ms. Kaveri S. Nehe
, Ms. Sneha R. Birajdar , Ms. Madhuri K. Ugale , Suwarna S. Ugale , Mr. Kishor N. Shedge
Artificial Neural Networks; GLCM features; Graphical User Interface; Machine Learning; Support Vector Machine.
Digital images are easy to manipulate and edit due to availability of powerful image processing and editing software such as Photoshop. Nowadays, it is possible to add or remove important part from an image without leaving any obvious traces of tampering. Authenticating digital images, validating their contents, and detecting forgeries is one of the critical challenges for governmental and nongovernmental organizations and departments. The image integrity verification as well as identifying the areas of tampering on images without need to any expert support or manual process or prior knowledge original image contents is now days becoming the challenging research problem. The method given in paper is focusing on authenticity of images and are based on concept of using illumination color estimation. Recently new method introduced for efficient forgery detection particular for faces in images. The illuminant color is estimated using the physics based method as well as statistical edge method which make the use of inverse intensity-chromaticity color space. The estimate of illuminant color is extracted independently from the different mini regions. For the classification used the Support Vector Machine (SVM) approach. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. Powerful digital image editing software makes images modifications straightforward. Questions pictures as evidence for real world events, this undermines our trust in photographs and in particular.
"Review Paper on Forgery Image Detection and Classification using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 6, page no.539 - 542, June-2020, Available :https://ijsdr.org/papers/IJSDR2006087.pdf
Volume 5
Issue 6,
June-2020
Pages : 539 - 542
Paper Reg. ID: IJSDR_192020
Published Paper Id: IJSDR2006087
Downloads: 000347193
Research Area: Engineering
Country: -, -, --
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
