TY - GEN
T1 - A Review on Automatic Image Forgery Classification Using Advanced Deep Learning Techniques
AU - Singh, Anshul Kumar
AU - Sharma, Chandani
AU - Singh, Brajesh Kumar
AU - Suryani, Erma
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Digital images are the representation of an event and considered as an evidence for most of the cases and scenarios. Copy-move forgery is a generic sort of forgery method. The technique for recreating one segment or part of the picture inside a similar picture is called as copy-move forgery. An effective and dependable technique has been created by various authors for recognizing these forgeries for restoring the image credibility. Passive approaches of image forgery detection are very hard to achieve. Copy-move, cut-paste, image splicing, image retouching and lightening condition are the examples of independent forgery techniques. Various techniques have been used by various authors like deep learning, convolution neural network, median filtering detection based on CNN, copy-move forgery detection, ringed residual, discrete cosine transform, U-Net, image splicing forgery detection, etc., with good accuracy on publically accessible databases like CASIA, dataset series of MICC, CoMoFoD, BSDS300, etc. In this paper, we have done a critical analysis of these image forgery detection technologies and the dataset available publically. Comparative analysis based on techniques, model, dataset and accuracy has been performed, and they achieve good accuracy as well.
AB - Digital images are the representation of an event and considered as an evidence for most of the cases and scenarios. Copy-move forgery is a generic sort of forgery method. The technique for recreating one segment or part of the picture inside a similar picture is called as copy-move forgery. An effective and dependable technique has been created by various authors for recognizing these forgeries for restoring the image credibility. Passive approaches of image forgery detection are very hard to achieve. Copy-move, cut-paste, image splicing, image retouching and lightening condition are the examples of independent forgery techniques. Various techniques have been used by various authors like deep learning, convolution neural network, median filtering detection based on CNN, copy-move forgery detection, ringed residual, discrete cosine transform, U-Net, image splicing forgery detection, etc., with good accuracy on publically accessible databases like CASIA, dataset series of MICC, CoMoFoD, BSDS300, etc. In this paper, we have done a critical analysis of these image forgery detection technologies and the dataset available publically. Comparative analysis based on techniques, model, dataset and accuracy has been performed, and they achieve good accuracy as well.
KW - CNN
KW - Copy-move forgery
KW - Cut-paste
KW - Image splicing
UR - http://www.scopus.com/inward/record.url?scp=85144225106&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-5292-0_1
DO - 10.1007/978-981-19-5292-0_1
M3 - Conference contribution
AN - SCOPUS:85144225106
SN - 9789811952913
T3 - Lecture Notes in Networks and Systems
SP - 1
EP - 10
BT - Advances in Data and Information Sciences - Proceedings of ICDIS 2022
A2 - Tiwari, Shailesh
A2 - Trivedi, Munesh C.
A2 - Kolhe, Mohan L.
A2 - Singh, Brajesh Kumar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Data and Information Sciences, ICDIS 2022
Y2 - 6 May 2022 through 7 May 2022
ER -