# Transferability Ranking of Adversarial Examples

arXiv: https://arxiv.org/abs/2208.10878 · HTML: https://mlsanity.com/paper/2208.10878/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 23 of 23)

- [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)
- [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)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [ResNet strikes back: An improved training procedure in timm](https://mlsanity.com/paper/2110.00476/) (2021)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [The Space of Transferable Adversarial Examples](https://mlsanity.com/paper/1704.03453/) (2017)
- [Admix: Enhancing the Transferability of Adversarial Attacks](https://mlsanity.com/paper/2102.00436/) (2021)
- [Cross-Domain Transferability of Adversarial Perturbations](https://mlsanity.com/paper/1905.11736/) (2019)
- [DaST: Data-free Substitute Training for Adversarial Attacks](https://mlsanity.com/paper/2003.12703/) (2020)
- [The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1704.01664/) (2017)
- [A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks](https://mlsanity.com/paper/2106.02105/) (2021)
- [Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging](https://mlsanity.com/paper/2001.01293/) (2020)
- [Who's Afraid of Adversarial Transferability?](https://mlsanity.com/paper/2105.00433/) (2021)
- [Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks](https://mlsanity.com/paper/2106.07141/) (2021)
- [The Security of Deep Learning Defences for Medical Imaging](https://mlsanity.com/paper/2201.08661/) (2022)
- [Generating Adversarial Examples with Controllable Non-transferability](https://mlsanity.com/paper/2007.01299/) (2020)

## Built on by (most cited first, 1 of 1)

- [OTI: A Model-free and Visually Interpretable Measure of Image Attackability](https://mlsanity.com/paper/2601.17536/) (2026)

## Abstract (arXiv)

Adversarial transferability in black-box scenarios presents a unique challenge: while attackers can employ surrogate models to craft adversarial examples, they lack assurance on whether these examples will successfully compromise the target model. Until now, the prevalent method to ascertain success has been trial and error-testing crafted samples directly on the victim model. This approach, however, risks detection with every attempt, forcing attackers to either perfect their first try or face exposure. Our paper introduces a ranking strategy that refines the transfer attack process, enabling the attacker to estimate the likelihood of success without repeated trials on the victim's system. By leveraging a set of diverse surrogate models, our method can predict transferability of adversarial examples. This strategy can be used to either select the best sample to use in an attack or the best perturbation to apply to a specific sample. Using our strategy, we were able to raise the transferability of adversarial examples from a mere 20% - akin to random selection-up to near upper-bound levels, with some scenarios even witnessing a 100% success rate. This substantial improvement not only sheds light on the shared susceptibilities across diverse architectures but also demonstrates that attackers can forego the detectable trial-and-error tactics raising increasing the threat of surrogate-based attacks.