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Evasion Attacks (EA) are used to test the robustness of trained neuralnetworks by distorting input data to misguide the model into incorrectclassifications. Creating these attacks is a challenging task, especially withthe ever-increasing complexity of models and datasets. In this work, weintroduce a self-supervised, computationally economical method for generatingadversarial examples, designed for the unseen black-box setting. Adaptingtechniques from representation learning, our method generates on-manifold EAsthat are encouraged to resemble the data distribution. These attacks arecomparable in effectiveness compared to the state-of-the-art when attacking themodel trained on, but are significantly more effective when attacking unseenmodels, as the attacks are more related to the data rather than the modelitself. Our experiments consistently demonstrate the method is effective acrossvarious models, unseen data categories, and even defended models, suggesting asignificant role for on-manifold EAs when targeting unseen models.

OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks