Adversarial Detection

Adversarial detection focuses on identifying maliciously perturbed inputs—adversarial examples—that fool machine learning models, aiming to enhance the robustness and security of AI systems. Current research emphasizes developing efficient and generalizable detection methods, exploring diverse approaches such as self-supervised learning, neural codecs, and analysis of model predictions and feature attributions, often employing architectures like autoencoders, LSTMs, and diffusion models. These advancements are crucial for securing AI applications across various domains, from autonomous driving and speaker verification to face recognition and network security, mitigating the risks posed by adversarial attacks.

Papers

April 9, 2023

Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval
Yanru Xiao, Cong Wang, Xing Gao
Unsupervised Setting Image Retrieval Adversarial Detection Retrieval System Deep Hashing

March 27, 2023

EMShepherd: Detecting Adversarial Samples via Side-channel Leakage
Ruyi Ding, Cheng Gongye, Siyue Wang, Aidong Ding, Yunsi Fei
Adversarial Attack Adversarial Sample Side Channel Adversarial Detection Wave Propagation

February 25, 2023

Scalable Attribution of Adversarial Attacks via Multi-Task Learning
Zhongyi Guo, Keji Han, Yao Ge, Wei Ji, Yun Li
Adversarial Attack Adversarial Example Multi Task Learning Multi Task Adversarial Detection

December 13, 2022

Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
Peter Lorenz, Margret Keuper, Janis Keuper
Convolutional Neural Network Adversarial Attack Adversarial Perturbation Adversarial Detection

November 18, 2022

Adversarial Detection by Approximation of Ensemble Boundary
T. Windeatt
Adversarial Attack Adversarial Perturbation Average Approximation Decision Boundary Adversarial Image Adversarial Detection

November 10, 2022

Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses
Abhiram Kolli, Muhammad Jehanzeb Mirza, Horst Possegger, Horst Bischof
Adversarial Attack Native Robustness Adversarial Example Real Human Adversarial Sample Test Time Adversarial Detection Adversarial Example Detection

October 27, 2022

TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems
Islam Debicha, Richard Bauwens, Thibault Debatty, Jean-Michel Dricot, Tayeb Kenaza, Wim Mees
Adversarial Attack Adversarial Example Network Intrusion Detection Adversarial Detection Evasion Attack

October 16, 2022

Nowhere to Hide: A Lightweight Unsupervised Detector against Adversarial Examples
Hui Liu, Bo Zhao, Kehuan Zhang, Peng Liu
Adversarial Example Adversarial Image Adversarial Detection Unsupervised Detection

October 4, 2022

CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, Joao Monteiro
Contrastive Learning Anomaly Detection Adversarial Perturbation Self Supervised Contrastive Learning Adversarial Detection Unsupervised Out of Distribution Detection

September 5, 2022

Adversarial Detection: Attacking Object Detection in Real Time
Han Wu, Syed Yunas, Sareh Rowlands, Wenjie Ruan, Johan Wahlstrom
Adversarial Attack Real World Real Time Object Detection Model Adversarial Detection

June 27, 2022

Adversarial Example Detection in Deployed Tree Ensembles
Laurens Devos, Wannes Meert, Jesse Davis
Adversarial Example Ensemble Learning Tree Ensemble Adversarial Detection Adversarial Example Detection

June 6, 2022

Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning
Thomas Hickling, Nabil Aouf, Phillippa Spencer
Adversarial Attack Deep Reinforcement Learning Task Planning Obstacle Avoidance Various Fast Moving Drone Adversarial Detection Explainable Deep Reinforcement Learning

June 1, 2022

Attack-Agnostic Adversarial Detection
Jiaxin Cheng, Mohamed Hussein, Jay Billa, Wael AbdAlmageed
Adversarial Attack Adversarial Example Adversarial Perturbation Adversarial Detection Adversarial Attack Detection

April 25, 2022

A Simple Structure For Building A Robust Model
Xiao Tan, Jingbo Gao, Ruolin Li
Adversarial Attack Deep Learning Model Adversarial Training Adversarial Detection Simple Structure

April 17, 2022

Residue-Based Natural Language Adversarial Attack Detection
Vyas Raina, Mark Gales
Adversarial Attack Adversarial Example Adversarial Detection Language Based

March 23, 2022

Input-specific Attention Subnetworks for Adversarial Detection
Emil Biju, Anirudh Sriram, Pratyush Kumar, Mitesh M Khapra
Adversarial Attack Adversarial Example Adversarial Input Adversarial Detection

February 16, 2022

Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing
Zhiyan Chen, Burak Kantarci
Generative Adversarial Network Generative Adversarial Adversarial Detection State of the Art GAN Detection Network Mobile Crowdsensing GAN Discriminator

February 9, 2022

Adversarial Detection without Model Information
Abhishek Moitra, Youngeun Kim, Priyadarshini Panda
Generative Adversarial Network Full Model Adversarial Detection Energy Based

February 5, 2022

Adversarial Detector with Robust Classifier
Takayuki Osakabe, Maungmaung Aprilpyone, Sayaka Shiota, Hitoshi Kiya
Adversarial Example Adversarial Detection Robust Classifier

January 4, 2022

Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection
Hui Liu, Bo Zhao, Yuefeng Peng, Weidong Li, Peng Liu
Adversarial Attack Adversarial Example Mixed Effect Human Understanding Adversarial Image Image Transformation Adversarial Detection