Class-incremental　learning　(CIL)　has　been　widely　applied　in　the　real　world　due　to　its　flexibility　and　scalability.　Recent　advancements　in　CIL　have　achieved　outstanding　performance.　However,　deep　neural　networks,　including　CIL　models,　face　challenges　in　resisting　adversarial　attacks.　Presently,　the　majority　of　research　in　CIL　focuses　on　alleviating　catastrophic　overfitting,　while　lacking　comprehensive　exploration　into　enhancing　adversarial　robustness.　To　this　end,　we　introduce　a　novel　CIL　framework　called　the　Perturbation　Volume-up　Framework　(PVF).　This　framework　divides　each　epoch　into　multiple　iterations,　wherein　three　main　tasks　are　performed　sequentially:　intensifying　adversarial　data,　extracting　new　knowledge,　and　reinforcing　old　knowledge.　To　intensify　adversarial　data,　we　propose　the　Fused　Robustness　Augmentation　(FRA)　approach.　This　method　incorporates　more　generalized　knowledge　into　the　adversarial　data　by　randomly　blending　data　and　leveraging　finely-tuned　Jensen-Shannon　(JS)　divergence.　For　the　remaining　two　tasks,　we　introduce　a　set　of　regularization　techniques　known　as　Knowledge　Inspiration　Regularization　(KIR).　This　regularization　employs　innovative　classification　and　distillation　losses　to　enhance　the　model＇s　generalization　performance　while　preserving　previously　learned　knowledge.　Extensive　experiments　have　demonstrated　the　effectiveness　of　our　method　in　enhancing　adversarial　robustness　of　CIL　models.