End-to-end double JPEG detection with a 3D convolutional network in the DCT domain

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Detection of double JPEG compression is essential in the field of digital image forensics. Although double JPEG compression detection methods have greatly improved with the development of convolutional neural networks (CNNs), they rely on handcrafted features such as discrete cosine transform (DCT) histograms. In this Letter, the authors propose an end-to-end trainable 3D CNN in the DCT domain for double JPEG compression detection. Moreover, they also propose a new type of module, called feature rescaling, to insert the quantisation table into the network suitably. The experiments show that the proposed method outperforms state-of-the-art methods.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2020-01
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.56, no.2, pp.82 - 85

ISSN
0013-5194
DOI
10.1049/el.2019.2719
URI
http://hdl.handle.net/10203/272245
Appears in Collection
CS-Journal Papers(저널논문)
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