Source Camera Identification With Dual-Tree Complex Wavelet Transform
Sensor pattern noise (SPN) extraction is a critical stage of the sensor based source camera identification (SCI). However, the quality of the extracted SPN with the traditional discrete wavelet transform (DWT) based method is poor around strong edges and along with the image border. To fill this gap...
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doaj-777cd3860bcd42a4a94eb19a6993c7052021-03-30T02:55:19ZengIEEEIEEE Access2169-35362020-01-018188741888310.1109/ACCESS.2020.29688558966247Source Camera Identification With Dual-Tree Complex Wavelet TransformHui Zeng0https://orcid.org/0000-0002-3776-4309Yongcai Wan1https://orcid.org/0000-0002-6661-4676Kang Deng2https://orcid.org/0000-0002-9734-0137Anjie Peng3https://orcid.org/0000-0001-9287-7536School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaSensor pattern noise (SPN) extraction is a critical stage of the sensor based source camera identification (SCI). However, the quality of the extracted SPN with the traditional discrete wavelet transform (DWT) based method is poor around strong edges and along with the image border. To fill this gap, we propose a dual tree complex wavelet transform (DTCWT) based method to extract the SPN from a given image, which achieves better performance in the area around strong edges. Furthermore, symmetric boundary extension instead of the periodized boundary extension is used for enhancing the quality of SPN along with the image border. Extensive experimental results on both synthetic noisy images and real-world photographs clearly demonstrate the superior SCI performance of the proposed method over state-of-the-arts. Moreover, the proposed method also shows potential in the application of image tampering localization.https://ieeexplore.ieee.org/document/8966247/Sensor pattern noisesource camera identificationdiscrete wavelet transformdual tree complex wavelet transform |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hui Zeng Yongcai Wan Kang Deng Anjie Peng |
spellingShingle |
Hui Zeng Yongcai Wan Kang Deng Anjie Peng Source Camera Identification With Dual-Tree Complex Wavelet Transform IEEE Access Sensor pattern noise source camera identification discrete wavelet transform dual tree complex wavelet transform |
author_facet |
Hui Zeng Yongcai Wan Kang Deng Anjie Peng |
author_sort |
Hui Zeng |
title |
Source Camera Identification With Dual-Tree Complex Wavelet Transform |
title_short |
Source Camera Identification With Dual-Tree Complex Wavelet Transform |
title_full |
Source Camera Identification With Dual-Tree Complex Wavelet Transform |
title_fullStr |
Source Camera Identification With Dual-Tree Complex Wavelet Transform |
title_full_unstemmed |
Source Camera Identification With Dual-Tree Complex Wavelet Transform |
title_sort |
source camera identification with dual-tree complex wavelet transform |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Sensor pattern noise (SPN) extraction is a critical stage of the sensor based source camera identification (SCI). However, the quality of the extracted SPN with the traditional discrete wavelet transform (DWT) based method is poor around strong edges and along with the image border. To fill this gap, we propose a dual tree complex wavelet transform (DTCWT) based method to extract the SPN from a given image, which achieves better performance in the area around strong edges. Furthermore, symmetric boundary extension instead of the periodized boundary extension is used for enhancing the quality of SPN along with the image border. Extensive experimental results on both synthetic noisy images and real-world photographs clearly demonstrate the superior SCI performance of the proposed method over state-of-the-arts. Moreover, the proposed method also shows potential in the application of image tampering localization. |
topic |
Sensor pattern noise source camera identification discrete wavelet transform dual tree complex wavelet transform |
url |
https://ieeexplore.ieee.org/document/8966247/ |
work_keys_str_mv |
AT huizeng sourcecameraidentificationwithdualtreecomplexwavelettransform AT yongcaiwan sourcecameraidentificationwithdualtreecomplexwavelettransform AT kangdeng sourcecameraidentificationwithdualtreecomplexwavelettransform AT anjiepeng sourcecameraidentificationwithdualtreecomplexwavelettransform |
_version_ |
1724184308867923968 |