用前向状态预测梯度，让对抗攻击速度提升5倍以上。
Fast Adversarial Attacks with Gradient Prediction
- 通过前向隐藏层预测输入梯度，跳过耗时的反向传播。
- 在保持接近FGSM攻击效果的同时，吞吐量提升532%。
- 适合需要大规模生成对抗样本的模型评估与训练场景。
大规模生成对抗样本是鲁棒性评估、对抗训练和红队测试的核心需求，但即使是快速攻击如FGSM，仍受限于反向传播带来的高开销。本文提出一类新攻击方法，通过轻量级线性回归从前向传播的隐藏状态中预测输入梯度，从而完全消除反向传播。该方法基于神经网络的核视角，在神经正切核（NTK）范围内为精确解，且对实际有限宽度模型依然有效。实验表明，该方法在仅使用极少时间的情况下，恢复了大部分FGSM的攻击性能，对应吞吐量提升532%。结果表明，梯度预测是一种简单而通用的加速对抗生成路径，适用于真实场景下的时钟时间约束。
原文摘要 · Abstract (English)
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.
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