Deep Composite Face Image Attacks: Generation, Vulnerability and Detection
This addresses security vulnerabilities in face recognition systems for biometric applications, presenting a novel attack method with incremental improvements in generation and detection benchmarking.
The paper tackles the problem of generating composite face image attacks (CFIA) using GANs to blend facial attributes from two subjects, creating 526,000 attack samples, and benchmarks their vulnerability on four face recognition systems with a new metric, showing significant attack potential.
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}