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Zero-shot learning (ZSL) has received extensive attention recently especiallyin areas of fine-grained object recognition, retrieval, and image captioning.Due to the complete lack of training samples and high requirement of defensetransferability, the ZSL model learned is particularly vulnerable againstadversarial attacks. Recent work also showed adversarially robustgeneralization requires more data. This may significantly affect the robustnessof ZSL. However, very few efforts have been devoted towards this direction. Inthis paper, we take an initial attempt, and propose a generic formulation toprovide a systematical solution (named ATZSL) for learning a robust ZSL model.It is capable of achieving better generalization on various adversarial objectsrecognition while only losing a negligible performance on clean images forunseen classes, by casting ZSL into a min-max optimization problem. To addressit, we design a defensive relation prediction network, which can bridge theseen and unseen class domains via attributes to generalize prediction anddefense strategy. Additionally, our framework can be extended to deal with thepoisoned scenario of unseen class attributes. An extensive group of experimentsare then presented, demonstrating that ATZSL obtains remarkably more favorabletrade-off between model transferability and robustness, over currentlyavailable alternatives under various settings.

ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries