PoolAtnRes: Towards Generalisable Differential Morphing Attack Detection

Raghavendra Ramachandra, Sushma Krupa Venkatesh, Guoqiang Li; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9294-9303

Morphing attacks can successfully deceive face recognition systems resulting in unreliable access control especially in the border control scenario. Consequently the development of Morphing Attack Detection (MAD) algorithms is crucial for detecting morphing attacks based on either a single facial image (S-MAD) or two facial images (Differential-MAD or D-MAD). In this work we proposed a novel D-MAD approach PoolAtnRes to reliably detect morphing attacks. The proposed PoolAtnRes architecture is constructed using three main functional blocks namely convolution pooling Hybrid Attention and Residual blocks which are serially connected to detect morphing attacks. Extensive experiments were performed on the newly constructed morphing dataset using nine morphing-generation techniques. The detection performance of the proposed PoolAtnRes model was compared with three state-of-the-art (SOTA) D-MAD techniques with different performance evaluation protocols to benchmark its generalizability to unseen morphing generation. The results obtained indicated the best performance of the proposed PoolAtnRes D-MAD.