This paper describes the ID R&D team submission to the Spoofing Aware Speaker Verification (SASV) challenge. Firstly, we present an approach that utilizes automatic speaker verification (ASV) system together with countermeasures (CM) subsystem in a single computational graph, called an anti-spoofing subnetwork. Subnetwork is a small network operating on feature maps from larger parent network, in this case trained for a speaker verification task. While requiring a small number of additionally trained parameters, subnetwork approach showed great performance in spoofing attacks detection task. Secondly, we cover training strategies for independently trained ASV and CM systems. In addition, we present a SASV-EER optimization approach using a fusion of multiple systems outputs and quality measurement functions (QMFs). Our best fusion achieves 0.136% EER on SASV-2022 evaluation set, while the smallest single-model system with 11.6M parameters achieves 0.223% EER.