# Enhancing targeted transferability via feature space fine-tuning

arXiv: https://arxiv.org/abs/2401.02727 · HTML: https://mlsanity.com/paper/2401.02727/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (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)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://mlsanity.com/paper/1908.06281/) (2019)
- [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://mlsanity.com/paper/2103.15571/) (2021)
- [Enhancing Adversarial Example Transferability with an Intermediate Level Attack](https://mlsanity.com/paper/1907.10823/) (2019)
- [Feature Importance-aware Transferable Adversarial Attacks](https://mlsanity.com/paper/2107.14185/) (2021)
- [Improving Adversarial Transferability via Neuron Attribution-Based Attacks](https://mlsanity.com/paper/2204.00008/) (2022)
- [On Success and Simplicity: A Second Look at Transferable Targeted Attacks](https://mlsanity.com/paper/2012.11207/) (2020)
- [Learning Transferable Adversarial Examples via Ghost Networks](https://mlsanity.com/paper/1812.03413/) (2018)
- [On Generating Transferable Targeted Perturbations](https://mlsanity.com/paper/2103.14641/) (2021)
- [Transferable Perturbations of Deep Feature Distributions](https://mlsanity.com/paper/2004.12519/) (2020)
- [Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective](https://mlsanity.com/paper/1703.09471/) (2017)
- [Yet Another Intermediate-Level Attack](https://mlsanity.com/paper/2008.08847/) (2020)
- [Enhancing the Self-Universality for Transferable Targeted Attacks](https://mlsanity.com/paper/2209.03716/) (2022)
- [Task-generalizable Adversarial Attack based on Perceptual Metric](https://mlsanity.com/paper/1811.09020/) (2018)
- [Enhance transferability of adversarial examples with model architecture](https://mlsanity.com/paper/2202.13625/) (2022)

## Built on by (most cited first, 7 of 7)

- [One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP](https://mlsanity.com/paper/2505.19840/) (2025)
- [On the Adversarial Transferability of Generalized "Skip Connections"](https://mlsanity.com/paper/2410.08950/) (2024)
- [OTI: A Model-free and Visually Interpretable Measure of Image Attackability](https://mlsanity.com/paper/2601.17536/) (2026)
- [Two Heads Are Better Than One: Averaging along Fine-Tuning to Improve Targeted Transferability](https://mlsanity.com/paper/2412.20807/) (2024)
- [Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach](https://mlsanity.com/paper/2504.14137/) (2025)
- [Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation](https://mlsanity.com/paper/2602.23117/) (2026)
- [Fast Preemptive Robustification: High-Frequency Response Anti-Aligns Shared Vulnerability](https://mlsanity.com/paper/2603.25244/) (2026)

## Abstract (arXiv)

Adversarial examples (AEs) have been extensively studied due to their potential for privacy protection and inspiring robust neural networks. Yet, making a targeted AE transferable across unknown models remains challenging. In this paper, to alleviate the overfitting dilemma common in an AE crafted by existing simple iterative attacks, we propose fine-tuning it in the feature space. Specifically, starting with an AE generated by a baseline attack, we encourage the features conducive to the target class and discourage the features to the original class in a middle layer of the source model. Extensive experiments demonstrate that only a few iterations of fine-tuning can boost existing attacks' targeted transferability nontrivially and universally. Our results also verify that the simple iterative attacks can yield comparable or even better transferability than the resource-intensive methods, which rest on training target-specific classifiers or generators with additional data. The code is available at: github.com/zengh5/TA_feature_FT.