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Face morphing attacks target to circumvent Face Recognition Systems (FRS) byemploying face images derived from multiple data subjects (e.g., accomplicesand malicious actors). Morphed images can be verified against contributing datasubjects with a reasonable success rate, given they have a high degree offacial resemblance. The success of morphing attacks is directly dependent onthe quality of the generated morph images. We present a new approach forgenerating strong attacks extending our earlier framework for generating facemorphs. We present a new approach using an Identity Prior Driven GenerativeAdversarial Network, which we refer to as MIPGAN (Morphing through IdentityPrior driven GAN). The proposed MIPGAN is derived from the StyleGAN with anewly formulated loss function exploiting perceptual quality and identityfactor to generate a high quality morphed facial image with minimal artefactsand with high resolution. We demonstrate the proposed approach's applicabilityto generate strong morphing attacks by evaluating its vulnerability againstboth commercial and deep learning based Face Recognition System (FRS) anddemonstrate the success rate of attacks. Extensive experiments are carried outto assess the FRS's vulnerability against the proposed morphed face generationtechnique on three types of data such as digital images, re-digitized (printedand scanned) images, and compressed images after re-digitization from newlygenerated MIPGAN Face Morph Dataset. The obtained results demonstrate that theproposed approach of morph generation poses a high threat to FRS.

MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN