# Diversifying the High-level Features for better Adversarial Transferability

arXiv: https://arxiv.org/abs/2304.10136 · HTML: https://mlsanity.com/paper/2304.10136/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 76 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 30 of 59)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [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)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [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)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [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)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Black-box Adversarial Attacks with Limited Queries and Information](https://mlsanity.com/paper/1804.08598/) (2018)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (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)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [Mitigating Adversarial Effects Through Randomization](https://mlsanity.com/paper/1711.01991/) (2017)
- [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://mlsanity.com/paper/1908.06281/) (2019)
- [Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser](https://mlsanity.com/paper/1712.02976/) (2017)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [Certified Defenses against Adversarial Examples](https://mlsanity.com/paper/1801.09344/) (2018)

## Built on by (most cited first, 30 of 34)

- [Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt](https://mlsanity.com/paper/2406.04031/) (2024)
- [Boosting Adversarial Transferability by Block Shuffle and Rotation](https://mlsanity.com/paper/2308.10299/) (2023)
- [Boosting Adversarial Transferability by Achieving Flat Local Maxima](https://mlsanity.com/paper/2306.05225/) (2023)
- [Visual Adversarial Attack on Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2411.18275/) (2024)
- [Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning](https://mlsanity.com/paper/2403.16257/) (2024)
- [Compromising Embodied Agents with Contextual Backdoor Attacks](https://mlsanity.com/paper/2408.02882/) (2024)
- [Effectiveness Assessment of Recent Large Vision-Language Models](https://mlsanity.com/paper/2403.04306/) (2024)
- [Rethinking the Backward Propagation for Adversarial Transferability](https://mlsanity.com/paper/2306.12685/) (2023)
- [Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving](https://mlsanity.com/paper/2501.13563/) (2025)
- [LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions](https://mlsanity.com/paper/2406.00934/) (2024)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [When World Models Dream Wrong: Physical-Conditioned Adversarial Attacks against World Models](https://mlsanity.com/paper/2602.18739/) (2026)
- [Understanding Model Ensemble in Transferable Adversarial Attack](https://mlsanity.com/paper/2410.06851/) (2024)
- [Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement](https://mlsanity.com/paper/2503.15404/) (2025)
- [WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents](https://mlsanity.com/paper/2605.23220/) (2026)
- [Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats](https://mlsanity.com/paper/2409.19526/) (2024)
- [Lie Detector: Unified Backdoor Detection via Cross-Examination Framework](https://mlsanity.com/paper/2503.16872/) (2025)
- [TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning](https://mlsanity.com/paper/2412.11471/) (2024)
- [Towards Robust Object Detection: Identifying and Removing Backdoors via Module Inconsistency Analysis](https://mlsanity.com/paper/2409.16057/) (2024)
- [Exploring Adversarial Transferability between Kolmogorov-arnold Networks](https://mlsanity.com/paper/2503.06276/) (2025)
- [Harnessing the Computation Redundancy in ViTs to Boost Adversarial Transferability](https://mlsanity.com/paper/2504.10804/) (2025)
- [Forward Learning for Gradient-based Black-box Saliency Map Generation](https://mlsanity.com/paper/2403.15603/) (2024)
- [Boosting the Local Invariance for Better Adversarial Transferability](https://mlsanity.com/paper/2503.06140/) (2025)
- [Text Adversarial Attacks with Dynamic Outputs](https://mlsanity.com/paper/2509.22393/) (2025)
- [Bridging the Task Gap: Multi-Task Adversarial Transferability in CLIP and Its Derivatives](https://mlsanity.com/paper/2509.23917/) (2025)
- [Improving the Transferability of Adversarial Attacks by an Input Transpose](https://mlsanity.com/paper/2503.00932/) (2025)
- [Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers](https://mlsanity.com/paper/2604.04630/) (2026)
- [Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective](https://mlsanity.com/paper/2607.05783/) (2026)
- [Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models](https://mlsanity.com/paper/2410.04884/) (2024)
- [Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence](https://mlsanity.com/paper/2506.10459/) (2025)

## Abstract (arXiv)

Given the great threat of adversarial attacks against Deep Neural Networks (DNNs), numerous works have been proposed to boost transferability to attack real-world applications. However, existing attacks often utilize advanced gradient calculation or input transformation but ignore the white-box model. Inspired by the fact that DNNs are over-parameterized for superior performance, we propose diversifying the high-level features (DHF) for more transferable adversarial examples. In particular, DHF perturbs the high-level features by randomly transforming the high-level features and mixing them with the feature of benign samples when calculating the gradient at each iteration. Due to the redundancy of parameters, such transformation does not affect the classification performance but helps identify the invariant features across different models, leading to much better transferability. Empirical evaluations on ImageNet dataset show that DHF could effectively improve the transferability of existing momentum-based attacks. Incorporated into the input transformation-based attacks, DHF generates more transferable adversarial examples and outperforms the baselines with a clear margin when attacking several defense models, showing its generalization to various attacks and high effectiveness for boosting transferability. Code is available at https://github.com/Trustworthy-AI-Group/DHF.