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arXiv AI By Zhichao Hou, Xiaorui Liu

Towards One-for-All Robustness Across a Continuum of Threat Levels

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The paper introduces the Threat Conditional Network (TCN), a model that achieves robust performance across a continuous range of adversarial threat levels. TCN splits representation learning into a threat‑invariant backbone and a lightweight threat‑conditional adaptor, using Fourier‑based embeddings and channel‑wise affine modulation to condition on perturbation budgets. Experiments on CIFAR‑10, CIFAR‑100, and Tiny‑ImageNet demonstrate that TCN matches or exceeds ensembles of budget‑specialized models while adding only 4.6% more parameters, and it generalizes to unseen budgets and mismatched threat conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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