# Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting

arXiv: https://arxiv.org/abs/2112.06011 · HTML: https://mlsanity.com/paper/2112.06011/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 35 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 28 of 28)

- [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)
- [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)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Provable defenses against adversarial examples via the convex outer adversarial polytope](https://mlsanity.com/paper/1711.00851/) (2017)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [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)
- [Mitigating Adversarial Effects Through Randomization](https://mlsanity.com/paper/1711.01991/) (2017)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [HopSkipJumpAttack: A Query-Efficient Decision-Based Attack](https://mlsanity.com/paper/1904.02144/) (2019)
- [Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser](https://mlsanity.com/paper/1712.02976/) (2017)
- [Certified Defenses against Adversarial Examples](https://mlsanity.com/paper/1801.09344/) (2018)
- [On the Robustness of Semantic Segmentation Models to Adversarial Attacks](https://mlsanity.com/paper/1711.09856/) (2017)
- [Simple Black-Box Adversarial Perturbations for Deep Networks](https://mlsanity.com/paper/1612.06299/) (2016)
- [Max-Mahalanobis Linear Discriminant Analysis Networks](https://mlsanity.com/paper/1802.09308/) (2018)
- [Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization](https://mlsanity.com/paper/1805.12302/) (2018)

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

- [Frequency Domain Model Augmentation for Adversarial Attack](https://mlsanity.com/paper/2207.05382/) (2022)
- [Structure Invariant Transformation for better Adversarial Transferability](https://mlsanity.com/paper/2309.14700/) (2023)
- [Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input](https://mlsanity.com/paper/2203.09123/) (2022)
- [A Survey on Transferability of Adversarial Examples across Deep Neural Networks](https://mlsanity.com/paper/2310.17626/) (2023)
- [SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation](https://mlsanity.com/paper/2312.04913/) (2023)
- [Learning to Transform Dynamically for Better Adversarial Transferability](https://mlsanity.com/paper/2405.14077/) (2024)
- [Improving Adversarial Transferability by Stable Diffusion](https://mlsanity.com/paper/2311.11017/) (2023)
- [TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization](https://mlsanity.com/paper/2212.09254/) (2022)
- [A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks](https://mlsanity.com/paper/2310.00633/) (2023)
- [Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup](https://mlsanity.com/paper/2305.14846/) (2023)
- [Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning](https://mlsanity.com/paper/2209.11964/) (2022)
- [Towards Good Practices in Evaluating Transfer Adversarial Attacks](https://mlsanity.com/paper/2211.09565/) (2022)
- [Reliable Evaluation of Adversarial Transferability](https://mlsanity.com/paper/2306.08565/) (2023)
- [As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?](https://mlsanity.com/paper/2403.12693/) (2024)
- [Improving Adversarial Transferability via Model Alignment](https://mlsanity.com/paper/2311.18495/) (2023)
- [Adversarial Attack Based on Prediction-Correction](https://mlsanity.com/paper/2306.01809/) (2023)
- [Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size](https://mlsanity.com/paper/2401.13205/) (2024)
- [Understanding Model Ensemble in Transferable Adversarial Attack](https://mlsanity.com/paper/2410.06851/) (2024)
- [Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge](https://mlsanity.com/paper/2206.08316/) (2022)
- [SoK: Pitfalls in Evaluating Black-Box Attacks](https://mlsanity.com/paper/2310.17534/) (2023)
- [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://mlsanity.com/paper/2311.11796/) (2023)
- [Improving Transferable Targeted Attacks with Feature Tuning Mixup](https://mlsanity.com/paper/2411.15553/) (2024)
- [Adversarial Attack via Dual-Stage Network Erosion](https://mlsanity.com/paper/2201.00097/) (2022)
- [Investigating Deep Watermark Security: An Adversarial Transferability Perspective](https://mlsanity.com/paper/2402.16397/) (2024)
- [Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing](https://mlsanity.com/paper/2405.06340/) (2024)
- [Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability](https://mlsanity.com/paper/2406.05535/) (2024)
- [Boosting the Local Invariance for Better Adversarial Transferability](https://mlsanity.com/paper/2503.06140/) (2025)
- [How to choose your best allies for a transferable attack?](https://mlsanity.com/paper/2304.02312/) (2023)
- [S4ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack](https://mlsanity.com/paper/2410.13891/) (2024)
- [Explainability and Robustness of Deep Visual Classification Models](https://mlsanity.com/paper/2301.01343/) (2023)

## Abstract (arXiv)

We introduce a three stage pipeline: resized-diverse-inputs (RDIM), diversity-ensemble (DEM) and region fitting, that work together to generate transferable adversarial examples. We first explore the internal relationship between existing attacks, and propose RDIM that is capable of exploiting this relationship. Then we propose DEM, the multi-scale version of RDIM, to generate multi-scale gradients. After the first two steps we transform value fitting into region fitting across iterations. RDIM and region fitting do not require extra running time and these three steps can be well integrated into other attacks. Our best attack fools six black-box defenses with a 93% success rate on average, which is higher than the state-of-the-art gradient-based attacks. Besides, we rethink existing attacks rather than simply stacking new methods on the old ones to get better performance. It is expected that our findings will serve as the beginning of exploring the internal relationship between attack methods. Codes are available at https://github.com/278287847/DEM.