IEA-DP: Information Entropy-driven Adaptive Differential Privacy Protection Scheme for social networks

Zhang, Jing; Si, Kunliang; Zeng, Zuanyang; Li, Tongxin; Ye, Xiucai

doi:10.1007/s11227-024-06202-w

Cited by 2 publications

(2 citation statements)

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“…Various intelligent algorithms can be utilized to hide the identity of people appearing in photos (Tu et al (2021), Zhang et al (2024b), Zhang et al (2024a)). The early methods of facial anonymization were simple detection and blurring or blackening of facial regions in images, in order to achieve the goal of hiding the true identity of the face.…”

Section: Face Anonymizationmentioning

confidence: 99%

“…The distribution and use of personal facial photos and videos have become a common phenomenon on social media. The widespread dissemination and use of these data have brought convenience but also raised concerns about personal privacy protection (Yang et al (2024), Zhang et al (2024a)). The face, as the part of the human body that contains the most biological information, is particularly important for the protection of facial privacy.…”

Section: Introductionmentioning

confidence: 99%

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PICLAnony: Controlled Anonymization of Face Attributes Based on Parametric Imitation Comparison Learning

Liu,

Ji,

Liang

2024

Preprint

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With the ubiquitous use of mobile devices and the widespread sharing of personal photos and videos on social media, the use of anonymized images to avoid identity disclosure has become essential. Aiming at the problem of low-quality and uncontrollable anonymous faces generated by existing anonymization algorithms, we propose PICLAnony, a controllable anonymization algorithm for face attributes based on parametric imitation comparison learning. It transfers the four visual information corresponding to identity, expression, pose, and illumination of the source image to the generated anonymized face image through parametric imitation contrast learning. And it edits these attribute features that reflecting sensitive behavioral intentions in a context-controlled manner. In the parameter imitation learning stage, high-quality and pose-controllable anonymized faces are generated by imitating the semantic parameters of real images. In the parameter comparison learning stage, the semantic parameters of the edited anonymized image are compared and learned with those of the source image, which solves the problem of insufficient decoupling of expression and illumination attributes in the editing process. In addition, a background control module is designed to keep the background controllable during the editing process of anonymous face facial attributes. We show the subjective results of our algorithm on both CelebA\_HQ and FFHQ datasets, and the subjective and objective results demonstrate that PICLAnony outperforms the state-of-the-art methods in terms of image quality and editing of facial attributes of anonymized faces.

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Section: Face Anonymizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%