Adversarial Attack

Adversarial attacks aim to deceive machine learning models by subtly altering input data, causing misclassifications or other erroneous outputs. Current research focuses on developing more robust models and detection methods, exploring various attack strategies across different model architectures (including vision transformers, recurrent neural networks, and graph neural networks) and data types (images, text, signals, and tabular data). Understanding and mitigating these attacks is crucial for ensuring the reliability and security of AI systems in diverse applications, from autonomous vehicles to medical diagnosis and cybersecurity.

Papers

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
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System
Khushnaseeb Roshan, Aasim Zafar, Sheikh Burhan Ul Haque
Adversarial Attack Real Time Intrusion Detection Network Intrusion Detection Adversarial Scenario Untargeted Adversarial Attack

October 4, 2023

Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Biagio Montaruli, Luca Demetrio, Maura Pintor, Luca Compagna, Davide Balzarotti, Battista Biggio
Adversarial Attack Fine Grained Black Box Optimization Solid Ground Adversarial Manipulation
Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing
Daile Osorio-Roig, Mahdi Ghafourian, Christian Rathgeb, Ruben Vera-Rodriguez, Christoph Busch, Julian Fierrez
Adversarial Attack Physiological Biometrics Facial Attribute Facial Recognition System Privacy Preserving Face

October 2, 2023

LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model
Muhammad Ahmed Shah, Roshan Sharma, Hira Dhamyal, Raphael Olivier, Ankit Shah, Joseph Konan, Dareen Alharthi, Hazim T Bukhari, Massa Baali, Soham Deshmukh, Michael Kuhlmann, Bhiksha Raj, Rita Singh
Large Language Model Adversarial Attack Fine Tuning Task Transferability Transferable Attack Private Model Adversarial Suffix Proxy Model Proxy Tuning
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things
Xianjian Xie, Xiaochen Xian, Dan Li, Andi Wang
Adversarial Attack Adversarial Behavior Learning Problem Subspace Identification
Fooling the Textual Fooler via Randomizing Latent Representations
Duy C. Hoang, Quang H. Nguyen, Saurav Manchanda, MinLong Peng, Kok-Seng Wong, Khoa D. Doan
Adversarial Attack Adversarial Example Adversarial Perturbation Latent Representation Word Level Adversarial Adversarial Text Perturbation

October 1, 2023

September 28, 2023

Investigating Human-Identifiable Features Hidden in Adversarial Perturbations
Dennis Y. Menn, Tzu-hsun Feng, Sriram Vishwanath, Hung-yi Lee
Adversarial Attack Adversarial Perturbation Untargeted Attack Defense Strategy
Robust Offline Reinforcement Learning -- Certify the Confidence Interval
Jiarui Yao, Simon Shaolei Du
Reinforcement Learning Adversarial Attack Deep Reinforcement Learning Native Robustness Adversarial Training Confidence Interval Robust Offline Reinforcement Learning
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Huihui Gong, Minjing Dong, Siqi Ma, Seyit Camtepe, Surya Nepal, Chang Xu
Adversarial Attack Adversarial Training Adversarial Perturbation Attention Hypernetworks Perturbation Robustness

September 26, 2023

Structure Invariant Transformation for better Adversarial Transferability
Xiaosen Wang, Zeliang Zhang, Jianping Zhang
Adversarial Attack Adversarial Transferability Structural Attack Input Transformation Transfer Based Attack Effective Adversarial
DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space
Liu Jun, Zhou Jiantao, Zeng Jiandian, Jinyu Tian
Adversarial Attack Adversarial Example Feature Space Black Box Attack Score Based Adversarial Feature Adversarial Capability

September 25, 2023

Adversarial Attacks on Video Object Segmentation with Hard Region Discovery
Ping Li, Yu Zhang, Li Yuan, Jian Zhao, Xianghua Xu, Xiaoqin Zhang
Adversarial Attack Adversarial Example Video Object Segmentation Region Selection ImageNet Attack
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors
Trong-Nghia To, Danh Le Kim, Do Thi Thu Hien, Nghi Hoang Khoa, Hien Do Hoang, Phan The Duy, Van-Hau Pham
Adversarial Attack Diverse Ensemble Adversarial Sample Malware Detection Malware Analysis Based Malware Metamorphic Malware

Adversarial Attack

Papers

Targeted Adversarial Attacks on Generalizable Neural Radiance Fields

Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System

Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors

Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing

LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model

Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things

Fooling the Textual Fooler via Randomizing Latent Representations

Counterfactual Image Generation for adversarially robust and interpretable Classifiers

A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks

Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks

Adversarial Machine Learning in Latent Representations of Neural Networks

Intrinsic Biologically Plausible Adversarial Robustness

On Continuity of Robust and Accurate Classifiers

Investigating Human-Identifiable Features Hidden in Adversarial Perturbations

Robust Offline Reinforcement Learning -- Certify the Confidence Interval

Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks

Structure Invariant Transformation for better Adversarial Transferability

DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space

Adversarial Attacks on Video Object Segmentation with Hard Region Discovery

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors