Family Resemblance or Fraud? Face Morphing Attacks on Kinship Verification

Gargi Surendra Yeole, AARTHI S, Shalvika Srivastav, Akshay Agarwal

Abstract:

Kinship verification using facial images is widely
applied in forensic analysis, immigration, and child trafficking
prevention. However, deep learning models for kinship veri-
fication are vulnerable to morphed images, where the facial
features of two individuals are blended to create realistic but
fake images. This work investigates the influence of different
morphing ratios (95% child + 5% random parent to 50%
child + 50% random parent) on kinship verification algorithms.
Developing a morphed dataset allows us to experiment with
deep learning and kinship-specific models on original and
morphed child images to determine the threshold beyond which
non-kin morphs are labeled kin. The experiments show a
continuous rise in misclassification rates with the increasing
percentage of parental features in morphed images, underscor-
ing the difficulties encountered by current kinship verification
systems. It is to be noted that the current study is the first
to present significant insights into the vulnerability of existing
kinship verification models against different morph ratios. It
highlights the necessity for more effective verification methods
to counter the risks associated with facial morphing in real-
world applications.