Improve the Spoofing Resistance of Multimodal Verification with Representation-Based Measures

Recently, the security of multimodal verification has become a growing concern since many fusion systems have been known to be easily deceived by partial spoof attacks, i.e. only a subset of modalities is spoofed. In this paper, we verify such a vulnerability and propose to use two representation-based measures to close this gap. Firstly, we use the collaborative representation fidelity with non-target subjects to measure the affinity of a query sample to the claimed client. We further consider sparse coding as a competing comparison among the client and the non-target subjects, and hence explore two sparsity-based measures for recognition. Last, we select the representation-based measure, and assemble its score and the affinity score of each modality to train a support vector machine classifier. Our experimental results on a chimeric multimodal database with face and ear traits demonstrate that in both regular verification and partial spoof attacks, the proposed method significantly outperforms the well-known fusion methods with conventional measure.