Feature enhancement and supervised contrastive learning for image splicing forgery detection-Reference-Cited by-同舟云学术

Feature enhancement and supervised contrastive learning for image splicing forgery detection

Published:2023-05 Issue: Volume:136 Page:104005
ISSN:1051-2004
Container-title:Digital Signal Processing
language:en
Short-container-title:Digital Signal Processing

Author:

Xu Yanzhi,Zheng Jiangbin^ORCID,Fang Aiqing,Irfan Muhammad

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Signal Processing,Artificial Intelligence,Applied Mathematics,Computer Vision and Pattern Recognition,Statistics, Probability and Uncertainty,Computational Theory and Mathematics

Reference46 articles.

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3. Image splicing detection using color edge inconsistency;Fang,2010

4. TSMAE: a novel anomaly detection approach for Internet of things time series data using memory-augmented autoencoder;Gao;IEEE Trans. Netw. Sci. Eng.,2022

5. A mutually supervised graph attention network for few-shot segmentation: the perspective of fully utilizing limited samples;Gao;IEEE Trans. Neural Netw. Learn. Syst.,2022

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1. MSU-Net: the multi-scale supervised U-Net for image splicing forgery localization;Pattern Analysis and Applications;2024-07-20

2. Anti-forensic Analysis for Image Splicing Detection Through Advanced Filters;Lecture Notes in Networks and Systems;2024

3. Digital Images Authentication Technique Based on Multi-Blocks DCT and Singular Value Decomposition;2023 5th Novel Intelligent and Leading Emerging Sciences Conference (NILES);2023-10-21

4. VGG16Unet: effective passive model for image splicing forgery localization;Journal of Electronic Imaging;2023-07-17