Family Resemblance or Fraud? Face Morphing Attacks on Kinship Verification

Kinship verification using facial images is widely applied in forensic analysis, immigration, and child trafficking prevention. However, deep learning models for kinship verification 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, underscoring 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.