mlsanityData to 14 Oct 2026

Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation

arXiv 2211.04973 ↗First posted 9 Nov 2022cs.LG math.OCcited by 0

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Abstract (arXiv)

Adversarial perturbation plays a significant role in the field of adversarial robustness, which solves a maximization problem over the input data. We show that the backward propagation of such optimization can accelerate 2× (and thus the overall optimization including the forward propagation can accelerate 1.5×), without any utility drop, if we only compute the output gradient but not the parameter gradient during the backward propagation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 29 references that are in the map. First posted on arXiv 9 Nov 2022.

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References · 24

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12 more
  1. 2018Generating Natural Language Adversarial Examplescited by 424
  2. 2019Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attackscited by 355
  3. 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 345
  4. 2021Enhancing the Transferability of Adversarial Attacks through Variance Tuningcited by 244
  5. 2018Adversarial Robustness Toolbox v1.0.0cited by 215
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  7. 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 124
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  10. 2020OpenAttack: An Open-source Textual Adversarial Attack Toolkitcited by 84
  11. 2020DeepRobust: A PyTorch Library for Adversarial Attacks and Defensescited by 78
  12. 2019advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchcited by 69

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