# Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

arXiv: https://arxiv.org/abs/1910.14667 · HTML: https://mlsanity.com/paper/1910.14667/

## Key facts

- Cited by 155 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 30 of 155)

- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Adversarial Texture for Fooling Person Detectors in the Physical World](https://mlsanity.com/paper/2203.03373/) (2022)
- [LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition](https://mlsanity.com/paper/2101.07922/) (2021)
- [Adversarial Sticker: A Stealthy Attack Method in the Physical World](https://mlsanity.com/paper/2104.06728/) (2021)
- [Improving Transferability of Adversarial Patches on Face Recognition with Generative Models](https://mlsanity.com/paper/2106.15058/) (2021)
- [FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack](https://mlsanity.com/paper/2109.07193/) (2021)
- [Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection](https://mlsanity.com/paper/2112.04532/) (2021)
- [Physical Adversarial Attack meets Computer Vision: A Decade Survey](https://mlsanity.com/paper/2209.15179/) (2022)
- [Black Box Adversarial Prompting for Foundation Models](https://mlsanity.com/paper/2302.04237/) (2023)
- [T-SEA: Transfer-based Self-Ensemble Attack on Object Detection](https://mlsanity.com/paper/2211.09773/) (2022)
- [Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks](https://mlsanity.com/paper/2108.06179/) (2021)
- [Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving](https://mlsanity.com/paper/2101.06784/) (2021)
- [Certified Patch Robustness via Smoothed Vision Transformers](https://mlsanity.com/paper/2110.07719/) (2021)
- [Towards Practical Certifiable Patch Defense with Vision Transformer](https://mlsanity.com/paper/2203.08519/) (2022)
- [Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling](https://mlsanity.com/paper/2307.01778/) (2023)
- [DAP: A Dynamic Adversarial Patch for Evading Person Detectors](https://mlsanity.com/paper/2305.11618/) (2023)
- [Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack](https://mlsanity.com/paper/2308.11894/) (2023)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://mlsanity.com/paper/2108.11765/) (2021)
- [PAD: Patch-Agnostic Defense against Adversarial Patch Attacks](https://mlsanity.com/paper/2404.16452/) (2024)
- [On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving](https://mlsanity.com/paper/2201.01850/) (2022)
- [DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks](https://mlsanity.com/paper/2102.02956/) (2021)
- [Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors](https://mlsanity.com/paper/2205.13618/) (2022)
- [Gradient Methods Provably Converge to Non-Robust Networks](https://mlsanity.com/paper/2202.04347/) (2022)
- [HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design](https://mlsanity.com/paper/2212.05709/) (2022)
- [A Survey on Physical Adversarial Attack in Computer Vision](https://mlsanity.com/paper/2209.14262/) (2022)
- [The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks](https://mlsanity.com/paper/2303.01456/) (2023)
- [Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World](https://mlsanity.com/paper/2201.08619/) (2022)
- [ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking](https://mlsanity.com/paper/2202.01811/) (2022)

## Abstract (arXiv)

We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Through extensive experiments, we benchmark the effectiveness of adversarially trained patches under both white-box and black-box settings, and quantify transferability of attacks between datasets, object classes, and detector models. Finally, we present a detailed study of physical world attacks using printed posters and wearable clothes, and rigorously quantify the performance of such attacks with different metrics.