Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram
Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we pre...
Main Authors: | Jun Young Park, Tae An Kang, Yong Ho Moon, Il Kyu Eom |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-03-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/12/4/492 |
Similar Items
-
CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature
by: Sang In Lee, et al.
Published: (2022-01-01) -
Unveiling Copy-Move Forgeries: Enhancing Detection With SuperPoint Keypoint Architecture
by: Anjali Diwan, et al.
Published: (2023-01-01) -
Detection of Copy-Move Forgery in Digital Images Using Scale Invariant Feature Transform Algorithm and the Spearman Relationship
by: A. Fattahi, et al.
Published: (2020-12-01) -
A Very Fast Copy-Move Forgery Detection Method for 4K Ultra HD Images
by: Laura Bertojo, et al.
Published: (2022-06-01) -
A two-stage detection method of copy-move forgery based on parallel feature fusion
by: Wujian Ye, et al.
Published: (2022-04-01)