In　traditional　scenes　(short-distance　applications),　the　current　Face　Anti-Spoofing　(FAS)　methods　have　achieved　satisfactory　performance.　However,　in　surveillance　scenes　(long-distance　applications),　those　methods　cannot　be　generalized　well　due　to　the　deviation　in　image　quality.　Some　methods　attempt　to　recover　lost　details　from　low-quality　images　through　image　reconstruction,　but　unknown　image　degradation　results　in　suboptimal　performance.　In　this　paper,　we　regard　image　quality　degradation　as　a　domain　generalization　problem.　Specifically,　we　propose　an　end-to-end　Adversarial　Domain　Generalization　Network　(ADGN)　to　improve　the　generalization　of　FAS.　We　first　divide　the　accessible　training　data　into　multiple　sub-source　domains　based　on　image　quality　scores.　Then,　a　feature　extractor　and　a　domain　discriminator　are　trained　to　make　the　extracted　features　from　different　sub-source　domains　undistinguishable　(i.e.,　quality-invariant　features),　thus　forming　an　adversarial　learning　procedure.　At　the　same　time,　we　have　introduced　the　transfer　learning　strategy　to　address　the　problem　of　insufficient　training　data.　Our　method　won　second　place　in　＇Track　Surveillance　Face　Anti-spoofing＇of　the　4th　Face　Anti-spoofing　Challenge@CVPR2023.　Our　final　submission　obtains　9.21%　APCER,　1.90%　BPCER,　and　5.56%　ACER,　respectively.　©　2023　IEEE.