Current palmprint recognition models achieve strong performance on constrained datasets,yet exhibit significant limitations in handling challenging palmprint samples with geometric distortions and textural degradations. Data augmentation is widely adopted to improve model generalization.However, existing augmentation methods struggle to generate palmprint-specific variations while preserving identity consistency,leading to suboptimal performance.To address these problems, we propose a unified adversarial augmentation framework.It first utilizes an adversarial training paradigm for palmprint recognition, optimizing for challenging augmented samples by incorporating the feedback from the recognition network.We enhance palmprint images with both geometric and textual variations.Specifically, it adopts a spatial transformation module and a new identity-preserving module, which synthesizes palmprints with diverse textural variations while maintaining consistent identity.For more effective adversarial augmentation, a dynamic sampling strategy is proposed.Extensive experiments demonstrate the superior performance of our method on both challenging and constrained palmprint datasets. Our code will be released.