# Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature

arXiv: https://arxiv.org/abs/2412.16958 · HTML: https://mlsanity.com/paper/2412.16958/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [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)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [DPatch: An Adversarial Patch Attack on Object Detectors](https://mlsanity.com/paper/1806.02299/) (2018)
- [AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning](https://mlsanity.com/paper/2308.07026/) (2023)
- [Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon](https://mlsanity.com/paper/2203.03818/) (2022)
- [Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink](https://mlsanity.com/paper/2103.06504/) (2021)
- [PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning](https://mlsanity.com/paper/2004.05682/) (2020)
- [DenseNet: Implementing Efficient ConvNet Descriptor Pyramids](https://mlsanity.com/paper/1404.1869/) (2014)
- [T-SEA: Transfer-based Self-Ensemble Attack on Object Detection](https://mlsanity.com/paper/2211.09773/) (2022)
- [BadRobot: Jailbreaking Embodied LLM Agents in the Physical World](https://mlsanity.com/paper/2407.20242/) (2024)
- [Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective](https://mlsanity.com/paper/2010.03316/) (2020)
- [A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks](https://mlsanity.com/paper/2106.02105/) (2021)
- [Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems](https://mlsanity.com/paper/2201.06192/) (2022)
- [Transferable Adversarial Facial Images for Privacy Protection](https://mlsanity.com/paper/2408.01428/) (2024)
- [RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World](https://mlsanity.com/paper/2307.07653/) (2023)
- [Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples](https://mlsanity.com/paper/2403.10801/) (2024)
- [PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation](https://mlsanity.com/paper/2412.16651/) (2024)
- [Adversarial Examples in the Physical World: A Survey](https://mlsanity.com/paper/2311.01473/) (2023)

## Built on by (most cited first, 13 of 13)

- [ADVEDM:Fine-grained Adversarial Attack against VLM-based Embodied Agents](https://mlsanity.com/paper/2509.16645/) (2025)
- [Test-Time Backdoor Detection for Object Detection Models](https://mlsanity.com/paper/2503.15293/) (2025)
- [Vanish into Thin Air: Cross-prompt Universal Adversarial Attacks for SAM2](https://mlsanity.com/paper/2510.24195/) (2025)
- [Towards Reliable Forgetting: A Survey on Machine Unlearning Verification](https://mlsanity.com/paper/2506.15115/) (2025)
- [SegTrans: Transferable Adversarial Examples for Segmentation Models](https://mlsanity.com/paper/2510.08922/) (2025)
- [DarkHash: A Data-Free Backdoor Attack Against Deep Hashing](https://mlsanity.com/paper/2510.08094/) (2025)
- [Erosion Attack for Adversarial Training to Enhance Semantic Segmentation Robustness](https://mlsanity.com/paper/2601.14950/) (2026)
- [Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models](https://mlsanity.com/paper/2604.22552/) (2026)
- [Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces](https://mlsanity.com/paper/2510.08067/) (2025)
- [Dual-View Inference Attack: Machine Unlearning Amplifies Privacy Exposure](https://mlsanity.com/paper/2512.16126/) (2025)
- [Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving](https://mlsanity.com/paper/2604.23105/) (2026)
- [Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation](https://mlsanity.com/paper/2602.23117/) (2026)
- [VFACamou: View-Fused Adversarial Camouflage for Environment-Adaptive Physical Evasion](https://mlsanity.com/paper/2606.19736/) (2026)

## Abstract (arXiv)

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEsBased on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method's superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method's effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks.