Adversarial DEfense

Adversarial defense in machine learning aims to create models robust against adversarial attacks—maliciously crafted inputs designed to cause misclassification. Current research focuses on developing both training-based defenses, such as adversarial training and techniques leveraging optimal transport or energy-based models, and test-time defenses, including input preprocessing and model reprogramming. These efforts are crucial for ensuring the reliability and security of machine learning systems across diverse applications, from image classification and natural language processing to structural health monitoring and malware detection, where vulnerabilities could have significant consequences.

Papers

July 14, 2023

Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Neng Dong, Liyan Zhang, Shuanglin Yan, Hao Tang, Jinhui Tang
Adversarial Learning Person Re Identification Adversarial DEfense Character Transformation Occluded Person Re Identification

July 11, 2023

ATWM: Defense against adversarial malware based on adversarial training
Kun Li, Fan Zhang, Wei Guo
Adversarial Example Adversarial Training Adversarial DEfense Adversarial Malware

July 10, 2023

Enhancing Adversarial Robustness via Score-Based Optimization
Boya Zhang, Weijian Luo, Zhihua Zhang
Adversarial Attack Adversarial Robustness Adversarial DEfense Score Based Diffusion Model Score Based

June 15, 2023

DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
Caixin Kang, Yinpeng Dong, Zhengyi Wang, Shouwei Ruan, Yubo Chen, Hang Su, Xingxing Wei
Adversarial Attack Adversarial Patch Adversarial DEfense Defense Method Patch Attack Adversarial Region

June 10, 2023

Boosting Adversarial Robustness using Feature Level Stochastic Smoothing
Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan, R. Venkatesh Babu
Native Robustness Adversarial Training Adversarial Robustness Adversarial DEfense Adversarial Detection Node Aware Bi Smoothing

June 1, 2023

Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective
Andi Zhang, Mingtian Zhang, Damon Wischik
Generative Model Adversarial Example Adversarial Perturbation Bayesian Perspective Adversarial DEfense Semantic Adversarial

May 25, 2023

Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness
Emanuele Ballarin, Alessio Ansuini, Luca Bortolussi
Adversarial Training Adversarial Robustness Adversarial Learning Generative Adversarial Adversarial DEfense Adversarial Purification

May 23, 2023

Adversarial Defenses via Vector Quantization
Zhiyi Dong, Yongyi Mao
Vector Quantization Adversarial DEfense Box Attack

May 22, 2023

Mist: Towards Improved Adversarial Examples for Diffusion Models
Chumeng Liang, Xiaoyu Wu
Diffusion Model Adversarial Example Adversarial DEfense Thin Air

April 18, 2023

Masked Language Model Based Textual Adversarial Example Detection
Xiaomei Zhang, Zhaoxi Zhang, Qi Zhong, Xufei Zheng, Yanjun Zhang, Shengshan Hu, Leo Yu Zhang
Adversarial Attack Adversarial Example Masked Language Adversarial DEfense Textual Adversarial Example

March 21, 2023

Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense
Binh M. Le, Shahroz Tariq, Simon S. Woo
Adversarial Training Adversarial Perturbation Optimal Transport Optimal Trajectory Adversarial DEfense Jacobian Regularization

March 17, 2023

Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified $\ell_p$ Attacks
Ren Wang, Yuxuan Li, Sijia Liu
Adversarial Attack Adversarial Robustness Adversarial DEfense Neural Network Robustness Norm Attack \Ell_p$ Robustness

March 16, 2023

Among Us: Adversarially Robust Collaborative Perception by Consensus
Yiming Li, Qi Fang, Jiamu Bai, Siheng Chen, Felix Juefei-Xu, Chen Feng
Visual Perception Adversarial DEfense Consensus Group Decision Collaborative Perception Collaborative 3D Object Detection

March 8, 2023

Immune Defense: A Novel Adversarial Defense Mechanism for Preventing the Generation of Adversarial Examples
Jinwei Wang, Hao Wu, Haihua Wang, Jiawei Zhang, Xiangyang Luo, Bin Ma
Adversarial Attack Adversarial Example Faithful Generation Adversarial DEfense White Box

February 2, 2023

Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
Zunzhi You, Daochang Liu, Bohyung Han, Chang Xu
Adversarial Attack Adversarial Training Adversarial Robustness Adversarial DEfense Noisy Image Pre Trained Feature

January 30, 2023

On the Efficacy of Metrics to Describe Adversarial Attacks
Tommaso Puccetti, Tommaso Zoppi, Andrea Ceccarelli
Adversarial Attack Metric Library Adversarial DEfense Adversarial Image Attack Dataset

January 9, 2023

On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense
Asadullah Hill Galib, Bidhan Bashyal
Adversarial Attack Native Robustness Backdoor Attack Adversarial DEfense Time Series Model

December 30, 2022

Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Muzammal Naseer, Salman Khan, Fatih Porikli, Fahad Shahbaz Khan
Adversarial Attack Adversarial DEfense Human Guidance Adversarial Defense Surrogate Assisted Version Untargeted Attack Adversarial Direction Projected Gradient Descent Attack

December 20, 2022

Multi-head Uncertainty Inference for Adversarial Attack Detection
Yuqi Yang, Songyun Yang, Jiyang Xie. Zhongwei Si, Kai Guo, Ke Zhang, Kongming Liang
Adversarial Attack Adversarial Example Adversarial DEfense Adversarial Attack Detection

December 19, 2022

TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
Bairu Hou, Jinghan Jia, Yihua Zhang, Guanhua Zhang, Yang Zhang, Sijia Liu, Shiyu Chang
Adversarial Example Adversarial Robustness NLP Field Adversarial DEfense Robustness Evaluation Gradient Based Optimization Robustness Evaluation Framework