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.
Code (1)
Tasks
Face RecognitionSimilar Papers 제목 키워드 기반
Approximating Optimal Morphing Attacks using Template Inversion
Recent works have demonstrated the feasibility of inverting face recognition systems, enabling to recover convincing face images using only their embeddings. We leverage such template inversion models to develop a novel …
Face RecognitionMORPHControllable Inversion of Black-Box Face Recognition Models via Diffusion
Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be distinguished from one another. We tackle …
DenoisingDiversityFace RecognitionReal-World Transferable Adversarial Attack on Face-Recognition Systems
Adversarial attacks on face recognition (FR) systems pose a significant security threat, yet most are confined to the digital domain or require white-box access. We introduce GaP (Gaussian Patch), a novel method to gener…
Adversarial AttackFace RecognitionIs Face Recognition Safe from Realizable Attacks?
Face recognition is a popular form of biometric authentication and due to its widespread use, attacks have become more common as well. Recent studies show that Face Recognition Systems are vulnerable to attacks and can l…
Face RecognitionRSTAM: An Effective Black-Box Impersonation Attack on Face Recognition using a Mobile and Compact Printer
Face recognition has achieved considerable progress in recent years thanks to the development of deep neural networks, but it has recently been discovered that deep neural networks are vulnerable to adversarial examples.…
Face Recognition