mlsanityData to 7 Oct 2026

Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability

arXiv 2207.11694 ↗First posted 24 Jul 2022cs.LG cs.AI cs.CVcited by 12

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Abstract (arXiv)

Although many methods have been proposed to enhance the transferability of adversarial perturbations, these methods are designed in a heuristic manner, and the essential mechanism for improving adversarial transferability is still unclear. This paper summarizes the common mechanism shared by twelve previous transferability-boosting methods in a unified view, i.e., these methods all reduce game-theoretic interactions between regional adversarial perturbations. To this end, we focus on the attacking utility of all interactions between regional adversarial perturbations, and we first discover and prove the negative correlation between the adversarial transferability and the attacking utility of interactions. Based on this discovery, we theoretically prove and empirically verify that twelve previous transferability-boosting methods all reduce interactions between regional adversarial perturbations. More crucially, we consider the reduction of interactions as the essential reason for the enhancement of adversarial transferability. Furthermore, we design the interaction loss to directly penalize interactions between regional adversarial perturbations during attacking. Experimental results show that the interaction loss significantly improves the transferability of adversarial perturbations.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 16 of its 64 references that are in the map. First posted on arXiv 24 Jul 2022.

Builds on

16 of 64 references.

References · 16

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  4. 2016Adversarial examples in the physical worldcited by 1,762
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  7. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  8. 2017One pixel attack for fooling deep neural networkscited by 579
  9. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 440
  10. 2020Explaining Explanations: Axiomatic Feature Interactions for Deep Networkscited by 103
  11. 2020Patch-wise Attack for Fooling Deep Neural Networkcited by 78
  12. 2018Understanding and Enhancing the Transferability of Adversarial Examplescited by 41
4 more
  1. 2020Improving Query Efficiency of Black-box Adversarial Attackcited by 34
  2. 2017Boosting Adversarial Attacks with Momentumcited by 23
  3. 2020Technical Note: Game-Theoretic Interactions of Different Orderscited by 11
  4. 2021Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifierscited by 7

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Citing papers per year: 2023: 5; 2024: 4; 2025: 3.

12 papers.