Evading Forensic Classifiers with Attribute-Conditioned Adversarial
  Faces
Fahad Shamshad Koushik Srivatsan Karthik Nandakumar
The ability of generative models to produce highly realistic synthetic face
images has raised security and ethical concerns. As a first line of defense
against such fake faces, deep learning based forensic classifiers have been
developed. While these forensic models can detect whether a face image is
synthetic or real with high accuracy, they are also vulnerable to adversarial
attacks. Although such attacks can be highly successful in evading detection by
forensic classifiers, they introduce visible noise patterns that are detectable
through careful human scrutiny. Additionally, these attacks assume access to
the target model(s) which may not always be true. Attempts have been made to
directly perturb the latent space of GANs to produce adversarial fake faces
that can circumvent forensic classifiers. In this work, we go one step further
and show that it is possible to successfully generate adversarial fake faces
with a specified set of attributes (e.g., hair color, eye size, race, gender,
etc.). To achieve this goal, we leverage the state-of-the-art generative model
StyleGAN with disentangled representations, which enables a range of
modifications without leaving the manifold of natural images. We propose a
framework to search for adversarial latent codes within the feature space of
StyleGAN, where the search can be guided either by a text prompt or a reference
image. We also propose a meta-learning based optimization strategy to achieve
transferable performance on unknown target models. Extensive experiments
demonstrate that the proposed approach can produce semantically manipulated
adversarial fake faces, which are true to the specified attribute set and can
successfully fool forensic face classifiers, while remaining undetectable by
humans. Code: https://github.com/koushiksrivats/face_attribute_attack.