Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space
Not on the map. Why?
Abstract (arXiv)
Reconstructing facial images from black-box recognition models poses a significant privacy threat. While many methods require access to embeddings, we address the more challenging scenario of model inversion using only similarity scores. This paper introduces DarkerBB, a novel approach that reconstructs color faces by performing zero-order optimization within a PCA-derived eigenface space. Despite this highly limited information, experiments on LFW, AgeDB-30, and CFP-FP benchmarks demonstrate that DarkerBB achieves state-of-the-art verification accuracies in the similarity-only setting, with competitive query efficiency.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 11 Jun 2025.
Builds on
14 of 28 references.
References · 14
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2014Understanding Deep Image Representations by Inverting Themcited by 403
- 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 246
- 2017On the Reconstruction of Face Images from Deep Face Templatescited by 67
- 2022Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attackscited by 60
- 2020Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognitioncited by 36
- 2023Controllable Inversion of Black-Box Face Recognition Models via Diffusioncited by 22
- 2023Label-Only Model Inversion Attacks via Knowledge Transfercited by 21
- 2020Black-Box Face Recovery from Identity Featurescited by 20
- 2024A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attackscited by 18
- 2024Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Modelcited by 9
Built on by
Citing papers per year: 2026: 1.
1 paper.