Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700125-5.doi: 10.11896/jsjkx.230700125

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Gaussian Enhancement Module for Reinforcing High-frequency Details in Camera ModelIdentification

HUANG Yuanhang1,2, BIAN Shan1,2,3, WANG Chuntao1,2   

  1. 1 College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China
    2 Key Laboratory of Smart Agricultural Technology in Tropical South China,Ministry of Agriculture and Rural Affairs,Guangzhou 510642,China
    3 Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security,Shenzhen,Guangdong 518060,China
  • Published:2024-06-06
  • About author:HUANG Yuanhang,born in 1997,postgraduate,is a member of CCF(No.C0385G).His main research interests include camera model identification and image forgery localization.
    BIAN Shan,born in 1986,Ph.D,asso-ciate professor,is a member of CCF(No.21153M).Her main research interests include video forensics and tampering detection.
  • Supported by:
    Guangdong Provincial Key Laboratory of Intelligent Information Processing(2023B1212060076),National Natural Science Foundation of China(62172165),Natural Science Foundation of Guangdong Province,China(2022A1515010325) and Guangzhou Basic and Applied Basic Research Project(202201010742).

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Abstract: In multimedia forensics,a high-pass filter is one of the commonly used pre-processing layers by convolutional neural network to depress the impact of image content and only highlight high-frequency features.However,some other useful information containing forgery traces would also be removed indiscriminately in the meantime.To address this issue,in this paper,a simple yet effective Gaussian enhancement module is proposed to extract “extended” high-frequency features,namely,reinforce high-frequency details while maintaining the original feature strength.The GEM comprises two successive low-pass Gaussian filters to acquire a blurry version of the feature map and further get the corresponding extended high-frequency residual.It can strengthen fragile and subtle low-level forgery features adaptively and prevent feature attenuation as well.Experiments are conducted on the camera-model identification dataset by plugging the module into several mainstream backbone networks,indicating that it supports “plug and play” and is non-related to the specific network architecture.The proposed GEM brings a significant improvement both in the performance and the robustness of networks with the slightly increased complexity of models.

Key words: Camera model identification, Deep learning, Image forensics, High-pass filter, Gauss enhancement

CLC Number: 

  • TP391.41
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