Copy-move forgery detection using binary discriminant features-Reference-Cited by-同舟云学术

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Copy-move forgery detection using binary discriminant features

Published:2018-11 Issue: Volume: Page:
ISSN:1319-1578
Container-title:Journal of King Saud University - Computer and Information Sciences
language:en
Short-container-title:Journal of King Saud University - Computer and Information Sciences

Author:

Raju Priya Mariam,Nair Madhu S.

Publisher

Elsevier BV

Subject

General Computer Science

Reference40 articles.

1. SLIC superpixels compared to state-of-the-art superpixel methods;Achanta;IEEE Trans. Pattern Anal. Mach. Intell.,2012

2. Sift-based forensic method for copy move attack detection and transformation recovery;Amerini;IEEE Trans. Inf. Forensics Secur.,2011

3. Copy-move forgery detection via texture description;Ardizzone,2010

4. Exploring duplicated regions in natural images;Bashar;IEEE Trans. Image Process.,2010

5. Surf: Speeded up robust features;Bay,2006

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1. Robust passive image authentication scheme based on serial companied approach;Multimedia Tools and Applications;2024-08-29

2. CFDMI-SEC: An optimal model for copy-move forgery detection of medical image using SIFT, EOM and CHM;PLOS ONE;2024-07-23

3. Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis;Journal of Electronic Imaging;2024-07-04

4. An Optimized Fuzzy C-Means with Deep Neural Network for Image Copy-Move Forgery Detection;International Journal of Electrical and Electronics Research;2024-03-28

5. A novel method for real-time object-based copy-move tampering localization in videos using fine-tuned YOLO V8;Forensic Science International: Digital Investigation;2024-03

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