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

December 11, 2022

DISCO: Adversarial Defense with Local Implicit Functions
Chih-Hui Ho, Nuno Vasconcelos
Adversarial Example Adversarial Perturbation Adversarial DEfense Adversarial Image Image Manifold Implicit Function

November 4, 2022

Adversarial Defense via Neural Oscillation inspired Gradient Masking
Chunming Jiang, Yilei Zhang
Neuromorphic Hardware Adversarial DEfense Neural Oscillation Brain Inspired Computing Gradient Masking

November 3, 2022

Visually Adversarial Attacks and Defenses in the Physical World: A Survey
Xingxing Wei, Bangzheng Pu, Jiefan Lu, Baoyuan Wu
Timely Survey Adversarial Attack Adversarial Example Adversarial Robustness Adversarial DEfense Physical World

October 12, 2022

Visual Prompting for Adversarial Robustness
Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, Sijia Liu
Adversarial Attack Adversarial Robustness Adversarial DEfense Visual Prompting Adversarial Prompting

September 14, 2022

Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries
Alexander Cann, Ian Colbert, Ihab Amer
Adversarial Example Adversarial Robustness Adversarial DEfense White Box Feature Transfer Pre Processing Zero Shot Adversarial Robustness

August 12, 2022

Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks
Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler
Deep Learning Model Adversarial DEfense Channel Estimation Next Generation Wireless Network Defensive Distillation

August 1, 2022

Attacking Adversarial Defences by Smoothing the Loss Landscape
Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales
Adversarial Attack Loss Landscape Adversarial DEfense Gradient Based Adversarial Gradient Obfuscation

July 25, 2022

July 13, 2022

Perturbation Inactivation Based Adversarial Defense for Face Recognition
Min Ren, Yuhao Zhu, Yunlong Wang, Zhenan Sun
Adversarial Attack Adversarial Perturbation Face Recognition Adversarial DEfense

June 22, 2022

AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture
Hui Xia, Rui Zhang, Shuliang Jiang, Zi Kang
Adversarial Attack Adversarial Example Black Box Adversarial DEfense Texture Rather Black Box Adversarial Attack Linear Form

June 17, 2022

Landscape Learning for Neural Network Inversion
Ruoshi Liu, Chengzhi Mao, Purva Tendulkar, Hao Wang, Carl Vondrick
Gradient Descent Loss Landscape Adversarial DEfense GAN Inversion Model Growth

June 9, 2022

CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models
Federico Nesti, Giulio Rossolini, Gianluca D'Amico, Alessandro Biondi, Giorgio Buttazzo
Adversarial Example Adversarial Robustness Vision Model Adversarial DEfense Dataset Generation Adversarial Defense Car Learning to Act Physical Adversarial Patch

April 14, 2022

Q-TART: Quickly Training for Adversarial Robustness and in-Transferability
Madan Ravi Ganesh, Salimeh Yasaei Sekeh, Jason J. Corso
Adversarial Robustness Task Transferability Adversarial DEfense Faster Training Adversarial Risk

April 10, 2022

Measuring the False Sense of Security
Carlos Gomes
Natural Gradient Security Related Adversarial DEfense False Sense Attack Framework Gradient Masking

March 19, 2022

Adversarial Defense via Image Denoising with Chaotic Encryption
Shi Hu, Eric Nalisnick, Max Welling
Adversarial Example Image Denoising Adversarial DEfense Adversarial Image Black Box Attack Gray Box Adversarial Evaluation Chaotic Behavior

March 12, 2022

A Survey of Adversarial Defences and Robustness in NLP
Shreya Goyal, Sumanth Doddapaneni, Mitesh M. Khapra, Balaraman Ravindran
Natural Language Processing Timely Survey Adversarial Attack Native Robustness Adversarial Perturbation Adversarial DEfense

March 3, 2022

Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation
KiYoon Yoo, Jangho Kim, Jiho Jang, Nojun Kwak
Adversarial Attack New Benchmark Adversarial Example Data Detection Text Classification Density Estimation Adversarial DEfense

February 12, 2022

Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
Rui Shao, Pramuditha Perera, Pong C. Yuen, Vishal M. Patel
Adversarial Training Open Set Adversarial DEfense Open Set Recognition

January 29, 2022

Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Mengting Xu, Tao Zhang, Zhongnian Li, Daoqiang Zhang
Adversarial DEfense Adversarial Defense Successful Adversarial Attack Projected Gradient Descent Attack