  Face manipulation attacks have drawn the attention of biometric researchers
because of their vulnerability to Face Recognition Systems (FRS). This paper
proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based
on facial attributes using Generative Adversarial Networks (GANs). Given the
face images corresponding to two unique data subjects, the proposed CFIA method
will independently generate the segmented facial attributes, then blend them
using transparent masks to generate the CFIA samples. We generate $526$ unique
CFIA combinations of facial attributes for each pair of contributory data
subjects. Extensive experiments are carried out on our newly generated CFIA
dataset consisting of 1000 unique identities with 2000 bona fide samples and
526000 CFIA samples, thus resulting in an overall 528000 face image samples.
{{We present a sequence of experiments to benchmark the attack potential of
CFIA samples using four different automatic FRS}}. We introduced a new metric
named Generalized Morphing Attack Potential (G-MAP) to benchmark the
vulnerability of generated attacks on FRS effectively. Additional experiments
are performed on the representative subset of the CFIA dataset to benchmark
both perceptual quality and human observer response. Finally, the CFIA
detection performance is benchmarked using three different single image based
face Morphing Attack Detection (MAD) algorithms. The source code of the
proposed method together with CFIA dataset will be made publicly available:
\url{https://github.com/jagmohaniiit/LatentCompositionCode}
