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New attack method predicts gradients, boosting adversarial generation speed

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for generating adversarial examples in machine learning models by predicting gradients from forward-pass hidden states. This technique bypasses the computationally expensive backward pass typically required for such attacks. The new approach, inspired by a kernel view of neural networks, significantly increases attack throughput by an estimated 532% while maintaining substantial attack performance. AI

IMPACT Accelerates robustness evaluation and adversarial training by enabling faster generation of adversarial examples.

RANK_REASON The cluster contains an academic paper detailing a new method for adversarial attacks on machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New attack method predicts gradients, boosting adversarial generation speed

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The cluster contains an academic paper detailing a new method for adversarial attacks on machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Konstantina Palla ·

    Fast Adversarial Attacks with Gradient Prediction

    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 backw…

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