  Face image synthesis is gaining more attention in computer security due to
concerns about its potential negative impacts, including those related to fake
biometrics. Hence, building models that can detect the synthesized face images
is an important challenge to tackle. In this paper, we propose a fusion-based
strategy to detect face image synthesis while providing resiliency to several
attacks. The proposed strategy uses a late fusion of the outputs computed by
several undisclosed models by relying on random polynomial coefficients and
exponents to conceal a new feature space. Unlike existing concealing solutions,
our strategy requires no quantization, which helps to preserve the feature
space. Our experiments reveal that our strategy achieves state-of-the-art
performance while providing protection against poisoning, perturbation,
backdoor, and reverse model attacks.
