Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space

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.

@article{razzhigaev2025_2506.09777,
  title={ Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space },
  author={ Anton Razzhigaev and Matvey Mikhalchuk and Klim Kireev and Igor Udovichenko and Andrey Kuznetsov and Aleksandr Petiushko },
  journal={arXiv preprint arXiv:2506.09777},
  year={ 2025 }
}