Two-stream neural networks to detect manipulation of JPEG compressed images

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With the rapid spread of image editing software, anyone can easily create, distribute, and forge images. Although techniques to detect image forgery have been widely studied, current techniques have significant limitations, such as specific file formats, manipulations, or compression qualities. Although deep learning techniques have been introduced to detect various manipulations, such as blurring, median filtering, and Gaussian noise, these techniques are only suitable to detect forgeries of uncompressed images, and are difficult to apply in practice because most images are compressed for distribution. Therefore, a two-stream neural network approach for image forensics that is robust to compression is proposed. The two-stream neural network is based on constrained convolutional neural network and Markov characteristics to consider compression. Experimental results show that the proposed method overcomes current technique limitations.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2018-03
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.54, no.6, pp.354 - 355

ISSN
0013-5194
DOI
10.1049/el.2017.4444
URI
http://hdl.handle.net/10203/241324
Appears in Collection
CS-Journal Papers(저널논문)
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