Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability
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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
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4 more
- 2020Improving Query Efficiency of Black-box Adversarial Attackcited by 34
- 2017Boosting Adversarial Attacks with Momentumcited by 23
- 2020Technical Note: Game-Theoretic Interactions of Different Orderscited by 11
- 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.
- 2023Does a Neural Network Really Encode Symbolic Concepts?cited by 27
- 2024Defining and Extracting generalizable interaction primitives from DNNscited by 14
- 2024Towards the Dynamics of a DNN Learning Symbolic Interactionscited by 13
- 2023Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?cited by 11
- 2023Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNscited by 10
- 2023Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Modelscited by 8
- 2023Adapting Step-size: A Unified Perspective to Analyze and Improve Gradient-based Methods for Adversarial Attackscited by 1
- 2024Evaluating the Correctness of Inference Patterns Used by LLMs for Judgmentcited by 1
- 2025Technical Report: Quantifying and Analyzing the Generalization Power of a DNNcited by 1
- 2025The Interaction Bottleneck of Deep Neural Networks: Discovery, Proof, and Modulationcited by 1
- 2024Disentangling Regional Primitives for Image Generationcited by 0
- 2025Optimizing the Adversarial Perturbation with a Momentum-based Adaptive Matrixcited by 0
- 2025The Interaction Bottleneck of Deep Neural Networks: Discovery, Proof, and Modulation21 Dec 2025
- 2025Optimizing the Adversarial Perturbation with a Momentum-based Adaptive Matrix16 Dec 2025
- 2025Technical Report: Quantifying and Analyzing the Generalization Power of a DNN11 May 2025
- 2024Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment6 Oct 2024
- 2024Disentangling Regional Primitives for Image Generation6 Oct 2024
- 2024Towards the Dynamics of a DNN Learning Symbolic Interactions27 Jul 2024
- 2024Defining and Extracting generalizable interaction primitives from DNNs29 Jan 2024
- 2023Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models3 May 2023
- 2023Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs26 Apr 2023
- 2023Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?3 Apr 2023
- 2023Does a Neural Network Really Encode Symbolic Concepts?25 Feb 2023
- 2023Adapting Step-size: A Unified Perspective to Analyze and Improve Gradient-based Methods for Adversarial Attacks27 Jan 2023