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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.14868 |
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| _version_ | 1866918501691686912 |
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| author | Ciosek, Kamil Petrov, Aleksandr V. Felicioni, Nicolò Palla, Konstantina |
| author_facet | Ciosek, Kamil Petrov, Aleksandr V. Felicioni, Nicolò Palla, Konstantina |
| contents | 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 $532\%$ increase in throughput. These results suggest gradient prediction as a simple and general route to significantly faster adversarial generation under realistic wall-clock constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14868 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast Adversarial Attacks with Gradient Prediction Ciosek, Kamil Petrov, Aleksandr V. Felicioni, Nicolò Palla, Konstantina Machine Learning 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 $532\%$ increase in throughput. These results suggest gradient prediction as a simple and general route to significantly faster adversarial generation under realistic wall-clock constraints. |
| title | Fast Adversarial Attacks with Gradient Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.14868 |