Adversarial Patch

Adversarial patches are small, carefully designed images that can fool deep learning models, causing misclassifications or incorrect predictions in computer vision tasks. Current research focuses on developing increasingly realistic and effective patches, often leveraging diffusion models and optimization techniques to create patches that are both potent and visually inconspicuous, targeting various applications like autonomous driving and traffic sign recognition. This research is crucial for understanding and mitigating vulnerabilities in AI systems, particularly in safety-critical applications where reliable performance is paramount. The development of robust defenses against these attacks is a parallel and equally important area of investigation.

Papers

December 29, 2023

MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World
Zheng Zhou, Hongbo Zhao, Ju Liu, Qiaosheng Zhang, Liwei Geng, Shuchang Lyu, Wenquan Feng
Object Detector Adversarial Patch Physical World Man in the Middle Attack Adversarial CAmouflage

December 28, 2023

DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation
Wenyi Tan, Yang Li, Chenxing Zhao, Zhunga Liu, Quan Pan
Adversarial Training Object Detection Adversarial Patch Ensemble Model Single Simple Patch Adversarial Patch Generation

December 27, 2023

Natural Adversarial Patch Generation Method Based on Latent Diffusion Model
Xianyi Chen, Fazhan Liu, Dong Jiang, Kai Yan
Adversarial Attack Latent Space Latent Diffusion Model Adversarial Patch Latent Point Diffusion Model

December 11, 2023

Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors
Kalibinuer Tiliwalidi
Object Detector Adversarial Patch Image Patch Physical World Attack Based Physical Attack

December 10, 2023

Dynamic Adversarial Attacks on Autonomous Driving Systems
Amirhosein Chahe, Chenan Wang, Abhishek Jeyapratap, Kaidi Xu, Lifeng Zhou
Adversarial Attack Autonomous Driving Adversarial Patch Efficient Autonomous Driving

December 5, 2023

Generating Visually Realistic Adversarial Patch
Xiaosen Wang, Kunyu Wang
Adversarial Example Adversarial Patch

November 30, 2023

Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Bilel Tarchoun, Quazi Mishkatul Alam, Nael Abu-Ghazaleh, Ihsen Alouani
Adversarial Attack Multi View Adversarial Patch Hydra MDP

November 29, 2023

November 20, 2023

ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches
Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
Adversarial Attack Adversarial Perturbation Outlier Detection Adversarial Patch Dimension Reduction Adversarial Patch Attack Patch Based Attack

November 2, 2023

Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction
Abhijith Sharma, Phil Munz, Apurva Narayan
Full Model Adversarial Patch Pseudo Goal Robust Prediction Patch Attack Adversarial Region

October 26, 2023

Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow
Erik Scheurer, Jenny Schmalfuss, Alexander Lis, Andrés Bruhn
Adversarial Robustness Optical Flow Adversarial Patch Adversarial Patch Attack

October 19, 2023

PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
Chong Xiang, Tong Wu, Sihui Dai, Jonathan Petit, Suman Jana, Prateek Mittal
Adversarial Patch Computational Efficiency Model Improvement Adversarial Patch Attack Certifiable Robustness State of the Art Robustness Defensive Patch

October 5, 2023

Targeted Adversarial Attacks on Generalizable Neural Radiance Fields
Andras Horvath, Csaba M. Jozsa
Adversarial Attack Neural Radiance Field Adversarial Patch 3D Scene Representation Fast Adversarial Training Generalizable Neural Radiance Field

October 4, 2023

SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
KL Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman, Hamed Pirsiavash
Vision Transformer Deep Model Adversarial Patch Transformer Inference Standard Adversarial Training

September 27, 2023

Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection
Lukas Strack, Futa Waseda, Huy H. Nguyen, Yinqiang Zheng, Isao Echizen
Adversarial Patch Adversarial Patch Attack Infrared Object Physical Adversarial Patch

August 14, 2023

AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
Ziqi Zhou, Shengshan Hu, Minghui Li, Hangtao Zhang, Yechao Zhang, Hai Jin
Contrastive Learning Generative Adversarial Network Cross Modal Adversarial Patch

August 10, 2023

Generating Transferable and Stealthy Adversarial Patch via Attention-guided Adversarial Inpainting
Yanjie Li, Mingxing Duan, Xuelong Dai, Bin Xiao
Adversarial Patch Adversarial Patch Attack Refinement Network

August 6, 2023

SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
Adversarial Attack Monocular Depth Estimation Adversarial Patch

August 1, 2023

Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches
Pia Hanfeld, Khaled Wahba, Marina M. -C. Höhne, Michael Bussmann, Wolfgang Hönig
Adversarial Attack Adversarial Patch Diving Deep Adversarial Multi

Adversarial Patch

Papers

MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World

DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation

Natural Adversarial Patch Generation Method Based on Latent Diffusion Model

Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors

Dynamic Adversarial Attacks on Autonomous Driving Systems

Generating Visually Realistic Adversarial Patch

Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems

RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition

NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields

ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches

Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction

Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow

PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses

Targeted Adversarial Attacks on Generalizable Neural Radiance Fields

SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers

Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection

AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning

Generating Transferable and Stealthy Adversarial Patch via Attention-guided Adversarial Inpainting

SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation

Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches