Abstract
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.
Abstract (translated)
从黑盒识别模型中重构面部图像构成了重大的隐私威胁。尽管许多方法需要访问嵌入（embeddings），但我们解决了仅使用相似度分数进行模型反转的更具挑战性的情况。本文介绍了 DarkerBB，这是一种新颖的方法，它通过在由 PCA 导出的特征脸空间内执行零阶优化来重建彩色面部图像。尽管信息极为有限，在 LFW、AgeDB-30 和 CFP-FP 基准测试上的实验表明，DarkerBB 在仅基于相似度的情景下达到了最先进的验证准确率，并且具有竞争力的查询效率。
URL
https://arxiv.org/abs/2506.09777
