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Forensics of visual privacy protection in digital images

  • Fei Xue
  • Wei LuEmail author
  • Honglin Ren
  • Huimei Xiao
  • Qin Zhang
  • Xianjin Liu
Article

Abstract

Visual privacy protection (VPP) is to protect individual’s privacy information in digital images or videos against being seen, such as portrait etc. Once being protected, the privacy may be imperceptible. Forensics of visual privacy protection is becoming a new challenge. As a main visual privacy protection technology, blur operation is always used in VPP. When using the existing approaches such as blur segmentation to detect the images, natural blur or other solid color regions will be falsely alarmed, resulting in low precision. In this paper, we present a novel metric invisibility degree (IvD) to measure the privacy protected blur degree of each pixel. The proposed IvD is defined by calculating the similarity between the test image and the re-blurred image in joint transformation and spatial domain, which could significantly enhance the difference between the privacy protected blur region and the other regions. Then, an effective method based on IvD is developed to automatically forensic and localize the visual privacy protection regions. Firstly, the IvD in block DCT domain of each pixel is calculated, and a IvD map is obtained. Secondly, using the IvD map, the test image is segmented and followed by morphological operation to decrease the mis-alarm regions. Finally, a spatial texture feature descriptor, including gray statistics, smoothness and information capacity, is developed, based on which the detection result is further refined. Experimental results show that the proposed method can detect the privacy regions accurately.

Keywords

Digital image forensics Visual privacy protection Privacy protected blur Invisibility degree (IvD) 

Notes

Acknowledgment

This work is supported by the National Natural Science Foundation of China (No. U1736118), the Key Areas R&D Program of Guangdong (No. 2019B010136002), the Key Scientific Research Program of Guangzhou (No. 201804020068), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), Shanghai Minsheng Science and Technology Support Program (17DZ1205500), Shanghai Sailing Program (17YF1420000), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Fei Xue
    • 1
    • 2
    • 3
  • Wei Lu
    • 1
    • 2
    • 3
    Email author
  • Honglin Ren
    • 1
    • 2
    • 3
  • Huimei Xiao
    • 1
    • 2
    • 3
  • Qin Zhang
    • 1
    • 2
    • 3
  • Xianjin Liu
    • 1
    • 2
    • 3
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhouChina
  3. 3.Ministry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhouChina

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