DETECTION OF RECOLORING AND COPY-MOVE FORGERY IN DIGITAL IMAGES
Binnar Nikita
, Gaikwad Tejaswini , Naik Ravina , Sadgir Sarita
Convolutional neural networks; neural networks; forgery detection; image compression; image processing
Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required., we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23
"DETECTION OF RECOLORING AND COPY-MOVE FORGERY IN DIGITAL IMAGES", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 12, page no.657 - 659, December-2022, Available :https://ijsdr.org/papers/IJSDR2212100.pdf
Volume 7
Issue 12,
December-2022
Pages : 657 - 659
Paper Reg. ID: IJSDR_203068
Published Paper Id: IJSDR2212100
Downloads: 000347258
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
