Fast Adversarial Attacks with Gradient Prediction

2026-05-14 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Research Methodology & Innovation · Depth: Expert, quick

Summary

A new family of adversarial attacks has been developed that significantly improves throughput by eliminating the need for a backward pass. These attacks predict the input gradient using a lightweight linear regression model applied to forward-pass hidden states. This method is motivated by a kernel view of neural networks and is exact within the Neural Tangent Kernel regime, while still proving effective for practical finite-width models. Empirically, these techniques achieve attack performance comparable to FGSM, but with a substantial reduction in computational time, resulting in a 532% increase in throughput. This approach offers a general solution for generating adversarial examples much faster under real-world wall-clock constraints.

Key takeaway

For research scientists focused on adversarial robustness and training, adopting gradient prediction methods can drastically reduce the computational cost and time required for generating adversarial examples. You should explore integrating this backward-pass-free approach to achieve a 532% throughput increase, enabling more extensive evaluations and faster iteration cycles in your model development.

Key insights

Gradient prediction from forward-pass hidden states enables significantly faster adversarial attacks.

Principles

• Backward pass elimination boosts throughput.
• Kernel view informs gradient prediction.

Method

Predict input gradients from forward-pass hidden states using lightweight linear regression, eliminating the backward pass for faster adversarial example generation.

In practice

• Evaluate model robustness faster.
• Accelerate adversarial training.
• Improve red-teaming efficiency.

Topics

• Fast Adversarial Attacks
• Gradient Prediction
• Neural Tangent Kernel
• Forward Pass Optimization
• Adversarial Robustness

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.