# Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective

arXiv: https://arxiv.org/abs/2210.04213 · HTML: https://mlsanity.com/paper/2210.04213/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 83 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 30 of 61)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [Black-box Adversarial Attacks with Limited Queries and Information](https://mlsanity.com/paper/1804.08598/) (2018)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)

## Built on by (most cited first, 30 of 37)

- [Content-based Unrestricted Adversarial Attack](https://mlsanity.com/paper/2305.10665/) (2023)
- [SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation](https://mlsanity.com/paper/2312.04913/) (2023)
- [Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability](https://mlsanity.com/paper/2307.07873/) (2023)
- [ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing](https://mlsanity.com/paper/2303.17096/) (2023)
- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning](https://mlsanity.com/paper/2308.12636/) (2023)
- [ASAM: Boosting Segment Anything Model with Adversarial Tuning](https://mlsanity.com/paper/2405.00256/) (2024)
- [TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation](https://mlsanity.com/paper/2312.02207/) (2023)
- [Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks](https://mlsanity.com/paper/2310.15444/) (2023)
- [Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement](https://mlsanity.com/paper/2503.15404/) (2025)
- [Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack](https://mlsanity.com/paper/2306.16050/) (2023)
- [Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly](https://mlsanity.com/paper/2311.01323/) (2023)
- [Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions](https://mlsanity.com/paper/2407.16725/) (2024)
- [Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness](https://mlsanity.com/paper/2311.06423/) (2023)
- [AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models](https://mlsanity.com/paper/2409.07002/) (2024)
- [Rethinking the Vulnerabilities of Face Recognition Systems:From a Practical Perspective](https://mlsanity.com/paper/2405.12786/) (2024)
- [Improving Transferability of Adversarial Examples via Bayesian Attacks](https://mlsanity.com/paper/2307.11334/) (2023)
- [Attacking Transformers with Feature Diversity Adversarial Perturbation](https://mlsanity.com/paper/2403.07942/) (2024)
- [Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning](https://mlsanity.com/paper/2310.09891/) (2023)
- [Multi-Target Federated Backdoor Attack Based on Feature Aggregation](https://mlsanity.com/paper/2502.16545/) (2025)
- [Use as Many Surrogates as You Want: Selective Ensemble Attack to Unleash Transferability without Sacrificing Resource Efficiency](https://mlsanity.com/paper/2505.12644/) (2025)
- [Concept-based Adversarial Attack: a Probabilistic Perspective](https://mlsanity.com/paper/2507.02965/) (2025)
- [Towards the Transferable Audio Adversarial Attack via Ensemble Methods](https://mlsanity.com/paper/2304.08811/) (2023)
- [Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks](https://mlsanity.com/paper/2307.12872/) (2023)
- [Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR](https://mlsanity.com/paper/2401.17038/) (2024)
- [Learning to Learn Transferable Generative Attack for Person Re-Identification](https://mlsanity.com/paper/2409.04208/) (2024)
- [S4ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack](https://mlsanity.com/paper/2410.13891/) (2024)
- [Interpretable Spectrum Transformation Attacks to Speaker Recognition](https://mlsanity.com/paper/2302.10686/) (2023)
- [Towards Generic and Controllable Attacks Against Object Detection](https://mlsanity.com/paper/2307.12342/) (2023)
- [With Great Backbones Comes Great Adversarial Transferability](https://mlsanity.com/paper/2501.12275/) (2025)

## Abstract (arXiv)

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective. Here, we investigate the transferability from the data distribution perspective and hypothesize that pushing the image away from its original distribution can enhance the adversarial transferability. To be specific, moving the image out of its original distribution makes different models hardly classify the image correctly, which benefits the untargeted attack, and dragging the image into the target distribution misleads the models to classify the image as the target class, which benefits the targeted attack. Towards this end, we propose a novel method that crafts adversarial examples by manipulating the distribution of the image. We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method. Our method can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios, surpassing the previous best method by up to 40% in some cases.