Popular　topics　in　the　field　of　countermeasures　include　feature　engineering　and　neural-network-based　models,　which　involve　neural　network　architectures　and　loss　criteria.　This　study　focuses　on　Res2Net　and　its　variant　models　to　examine　the　impact　of　model　generalization　on　countermeasure　performance　in　the　ASVspoof　2019　logical　access　and　physical　access　scenarios.　Results　reveal　that　while　Res2Net　exhibits　superior　generalization　compared　to　its　variants,　the　most　effective　countermeasure　combines　both　feature　engineering　and　model　optimization.　The　proposed　dynamic　modulated-Res2Net　utilizes　channel-wise　soft　attention　to　recalibrate　feature　maps,　offering　adaptive　adjustments　to　spoofing　cues　of　varying　scales.　Evaluation　on　the　logical　access　dataset　demonstrates　dynamic　modulated-Res2Net's　relative　improvement　of　over　38%　compared　to　Res2Net.　Furthermore,　we　exploit　low-frequency　features　and　combine　them　with　dynamic　modulated-Res2Net　to　achieve　in　an　equal　error　rate　of　1.21%　under　logical　access　and　0.41%　under　physical　access,　establishing　our　proposed　dynamic　modulated-Res2Net　as　one　of　the　top-performing　single　systems.　Additionally,　we　compare　the　best　countermeasures　in　different　scenarios,　highlighting　the　ongoing　challenge　of　achieving　generalization.　IEEE