9 research outputs found
Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution
A master face is a face image that passes face-based identity authentication
for a high percentage of the population. These faces can be used to
impersonate, with a high probability of success, any user, without having
access to any user information. We optimize these faces for 2D and 3D face
verification models, by using an evolutionary algorithm in the latent embedding
space of the StyleGAN face generator. For 2D face verification, multiple
evolutionary strategies are compared, and we propose a novel approach that
employs a neural network to direct the search toward promising samples, without
adding fitness evaluations. The results we present demonstrate that it is
possible to obtain a considerable coverage of the identities in the LFW or RFW
datasets with less than 10 master faces, for six leading deep face recognition
systems. In 3D, we generate faces using the 2D StyleGAN2 generator and predict
a 3D structure using a deep 3D face reconstruction network. When employing two
different 3D face recognition systems, we are able to obtain a coverage of
40%-50%. Additionally, we present the generation of paired 2D RGB and 3D master
faces, which simultaneously match 2D and 3D models with high impersonation
rates.Comment: accepted for publication in IEEE Transactions on Biometrics,
Behavior, and Identity Science (TBIOM). This paper extends arXiv:2108.01077
that was accepted to IEEE FG 202
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
In this study, we consider a continuous min--max optimization problem
whose objective
function is a black-box. We propose a novel approach to minimize the worst-case
objective function directly using a covariance matrix
adaptation evolution strategy (CMA-ES) in which the rankings of solution
candidates are approximated by our proposed worst-case ranking approximation
(WRA) mechanism. We develop two variants of WRA combined with CMA-ES and
approximate gradient ascent as numerical solvers for the inner maximization
problem. Numerical experiments show that our proposed approach outperforms
several existing approaches when the objective function is a smooth strongly
convex--concave function and the interaction between and is strong. We
investigate the advantages of the proposed approach for problems where the
objective function is not limited to smooth strongly convex--concave functions.
The effectiveness of the proposed approach is demonstrated in the robust
berthing control problem with uncertainty.ngly convex--concave functions. The
effectiveness of the proposed approach is demonstrated in the robust berthing
control problem with uncertainty
Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context
Surrogate modeling has become a valuable technique for black-box optimization
tasks with expensive evaluation of the objective function. In this paper, we
investigate the relationship between the predictive accuracy of surrogate
models and features of the black-box function landscape. We also study
properties of features for landscape analysis in the context of different
transformations and ways of selecting the input data. We perform the landscape
analysis of a large set of data generated using runs of a surrogate-assisted
version of the Covariance Matrix Adaptation Evolution Strategy on the noiseless
part of the Comparing Continuous Optimisers benchmark function testbed.Comment: 25 pages main article, 28 pages supplementary material, 3 figures,
currently under review at Evolutionary Computation journa