SNIS: A Signal Noise Separation-Based Network for Post-Processed Image Forgery Detection-Reference-Cited by-同舟云学术

SNIS: A Signal Noise Separation-Based Network for Post-Processed Image Forgery Detection

Published:2023-02 Issue:2 Volume:33 Page:935-951
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Chen Jiaxin¹^ORCID,Liao Xin¹^ORCID,Wang Wei²^ORCID,Qian Zhenxing¹^ORCID,Qin Zheng³^ORCID,Wang Yaonan⁴^ORCID

Affiliation:

1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

3. School of Computer Science, Fudan University, Shanghai, China

4. College of Electrical and Information Engineering and the National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Science and Technology Projects of Hunan Province

Science and Technology Key Projects of Changsha City

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10036154/09878273.pdf?arnumber=9878273

Reference56 articles.

1. Constrained R-Cnn: A General Image Manipulation Detection Model

2. Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN)

3. CASIA Image Tampering Detection Evaluation Database

4. Robust Detection of Image Operator Chain With Two-Stream Convolutional Neural Network

5. Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multiple images simultaneous encryption and decryption via deep-learning assisted interferenceless coded aperture correlation holography;Optics Communications;2024-12

2. Datasets, clues and state-of-the-arts for multimedia forensics: An extensive review;Expert Systems with Applications;2024-09

3. Fire Hawks Optimizer with hybrid deep learning driven fall detection on multimodal sensor data;Multimedia Tools and Applications;2024-08-19

4. Robust data hiding method based on frequency coefficient variance in repetitive compression;Engineering Science and Technology, an International Journal;2024-08

5. Enhanced an Intrusion Detection System for IoT networks through machine learning techniques: an examination utilizing the AWID dataset;Cogent Engineering;2024-07-20