Methods › Computer Vision › Generative Adversarial Networks › PrivacyNet

PrivacyNet

1 paper tagged archive 2025-07-28

Introduced by Vahid Mirjalili et al. in PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

PrivacyNet is a GAN-based semi-adversarial network (SAN) that modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender).

PaperSource

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

1 task the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Attribute1

Usage over time archive 2025-07-28

Papers per year tagged with PrivacyNet: 2020 to 2020, peak 1 1 0 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Generative Adversarial NetworksFace Privacy

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