Fast Adversarial Attacks with Gradient Prediction
Abstract
Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even "fast" attacks such as FGSM remain throughput-limited by the cost of a backward pass. We introduce a family of attacks that eliminates the backward pass by predicting the input gradient from forward-pass hidden states via a lightweight linear regression. The approach is motivated by a kernel view of neural networks and is exact in the Neural Tangent Kernel regime, while remaining effective for practical finite-width models. Empirically, our methods recover much of FGSM's attack performance while using only a small fraction of the time, corresponding to a increase in throughput. These results suggest gradient prediction as a simple and general route to significantly faster adversarial generation under realistic wall-clock constraints.
Cite
@article{arxiv.2605.14868,
title = {Fast Adversarial Attacks with Gradient Prediction},
author = {Kamil Ciosek and Aleksandr V. Petrov and Nicolò Felicioni and Konstantina Palla},
journal= {arXiv preprint arXiv:2605.14868},
year = {2026}
}
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17 pages