  This paper describes our DKU-OPPO system for the 2022 Spoofing-Aware Speaker
Verification (SASV) Challenge. First, we split the joint task into speaker
verification (SV) and spoofing countermeasure (CM), these two tasks which are
optimized separately. For ASV systems, four state-of-the-art methods are
employed. For CM systems, we propose two methods on top of the challenge
baseline to further improve the performance, namely Embedding Random Sampling
Augmentation (ERSA) and One-Class Confusion Loss(OCCL). Second, we also explore
whether SV embedding could help improve CM system performance. We observe a
dramatic performance degradation of existing CM systems on the
domain-mismatched Voxceleb2 dataset. Third, we compare different fusion
strategies, including parallel score fusion and sequential cascaded systems.
Compared to the 1.71% SASV-EER baseline, our submitted cascaded system obtains
a 0.21% SASV-EER on the challenge official evaluation set.
