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VPP_AHA: Visual Privacy Protection via Adaptive Histogram Adjustment

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Abstract

A novel visual privacy protection (VPP) algorithm is proposed for camouflage of the visual identifiers in digital images. The challenge is to suffice the de-identification with a different look. This issue has been previously addressed by two classes of algorithms, i.e. modifying target features with or without a reference. The former ran well by finding a surrogate from randomized hybrid features between the target and the reference, and the latter made it by blurring or hiding the details of the region. Inspired by camouflage in animals for self-concealment, this paper presents a simple and efficient algorithm that imitates such behavior via adaptive histogram shift adjustment without a reference. The image is first blurred and segmented into several flat surfaces, then adaptively re-joined or split by shifts randomly chosen to produce the de-identified output. It is found that the success of the surface-based feature redaction depends on the shift diversity, the saliency, and coverage of the surfaces segmented, which are used as adjusting parameters for the camouflage. Extensive examples of real and synthetic images have demonstrated that our results compare favorably to those obtained by existing VPP methods, with required security, robustness, and selective reversibility.

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Authors and Affiliations

  1. School of Cyber Security, Hainan University, Haikou, 570228, Hainan, China

    Xiaoming Yao

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Correspondence to Xiaoming Yao.

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Conclusion and future work

This paper has presented a novel visual privacy protection algorithm for de-identifying the visual objects in a digital image. The result of the object de-identification is an image in which the visual objects have been modified to bear a different look in a way that imitates the animal’s coloration adaptation process by randomly merging and separating the nearby surfaces using adaptive local histogram shift.

Our approach exploits the blurring techniques to segment the image into several visually ‘flat surfaces” after specifying the coverage, surface saliency, and shift diversity requirements of the visual objects. The same number of randomly generated shifts, big or small, is assigned to each of the “flat surfaces”, decides if the neighboring surfaces are merged or separated according to the conditions of similar or disruptive coloration in animals.

The technique is capable of not only merging nearby surfaces into one larger surface with details of lower or higher contrast but also separating nearby surfaces of visual resemblance into several visually different surfaces, which changes the visual objects into a different appearance. Comparative experiments show that a careful selection of the blurring parameters and the diversity constraints is necessary and sufficient to handle this task.

Our method performs almost as well as previous techniques for facial de-identification and in instances in which all visual identifiers are de-identified, as an ad hoc algorithm without references, it outperforms earlier work in terms of security, robustness, intelligibility, and reversibility.

Currently, we are investigating extensions for a smooth color shift in real color still images and for motion camouflage of visual objects from video, which promise to impose a new set of challenges.

Conflicts of interest /Competing interests

The authors have no relevant financial or non-financial interests to disclose, and no funding was received for conducting this study.

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Yao, X. VPP_AHA: Visual Privacy Protection via Adaptive Histogram Adjustment. Multimed Tools Appl 81, 6277–6303 (2022). https://doi.org/10.1007/s11042-021-11749-4

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