ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation

TL;DR

ReGenMorph introduces a novel GAN-based face morphing method that produces highly realistic morphed images, improving attack realism and challenging face recognition systems and detection methods.

Contribution

The paper presents ReGenMorph, a new GAN-based pipeline that eliminates artifacts in face morphing, resulting in more realistic attacks for evaluating face recognition and detection systems.

Findings

ReGenMorph produces visually realistic morphed faces with minimal artifacts.

ReGenMorph attacks increase vulnerability of face recognition systems.

ReGenMorph challenges face morphing detection methods.

Abstract

Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks. While creating a morphed face detector (MFD), training on all possible attack types is essential to achieve good detection performance. Therefore, investigating new methods of creating morphing attacks drives the generalizability of MADs. Creating morphing attacks was performed on the image level, by landmark interpolation, or on the latent-space level, by manipulating latent vectors in a generative adversarial network. The earlier results in varying blending artifacts and the latter results in synthetic-like striping artifacts. This work presents the novel morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using a GAN-based generation, as well as, eliminate the manipulation in the latent space, resulting in visibly realistic morphed images compared to previous works. The generated ReGenMorph appearance is compared to recent morphing approaches and evaluated for face recognition vulnerability and attack detectability, whether as known or unknown attacks.