arXiv (All)
open access
SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy
Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent. Generator-based protection methods rely on heavy pretrained backbones, multi-stage inference or visible semantic edits. We introduce SSM-Attack, a black-box face privacy
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SSM-Attack: Learning Where to Perturb for Black-Box Face Privacy#1318908
- Saving Face on Facebook: Privacy Concerns, Social Benefits, and Impression Management#300188
- How Private is Private? A Comparative Study for Face De-Identification#1234701
- The Other-Race Effect in Implicit Memory for Face Combinations#1179164
- Resisting Face Surveillance with Copyright Law#1121645
Record · ID 1342199
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive