Toward Effective Image Manipulation Detection With Proposal Contrastive Learning
Author:
Affiliation:
1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
2. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Funder
National Natural Science Foundation of China
Shenzhen Science and Technology Program
Peng Cheng National Lab (PCNL) Key Project
Research Center for Computer Network (Shenzhen) Ministry of Education.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Media Technology
Link
http://xplorestaging.ieee.org/ielx7/76/10241245/10049563.pdf?arnumber=10049563
Reference50 articles.
1. Exposing photo manipulation from user-guided 3D lighting analysis;carvalho;Proc SPIE,2015
2. Image splicing detection with local illumination estimation
3. Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries
4. Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN)
5. Constrained R-Cnn: A General Image Manipulation Detection Model
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