Adversarial Method

Adversarial methods are techniques used to improve the robustness and generalization of machine learning models by introducing carefully crafted perturbations during training. Current research focuses on applying these methods to diverse areas, including image retrieval, activity recognition, and natural language processing, often employing generative adversarial networks (GANs) or gradient-based attacks and defenses. This approach is crucial for enhancing the reliability and fairness of machine learning systems in real-world applications, particularly where data distribution shifts or malicious attacks are a concern, and is driving advancements in model interpretability and security.

Papers

March 14, 2023

Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review
João Vitorino, Tiago Dias, Tiago Fonseca, Eva Maia, Isabel Praça
Adversarial Attack Adversarial Learning Systematic Review Adversarial Method Applicability Study Automated Software Testing Adversarial Constraint

March 12, 2023

Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems
Islam Debicha, Benjamin Cochez, Tayeb Kenaza, Thibault Debatty, Jean-Michel Dricot, Wim Mees
Adversarial Attack Network Intrusion Detection Adversarial Method X Former

March 3, 2023

Domain adaptation using optimal transport for invariant learning using histopathology datasets
Kianoush Falahkheirkhah, Alex Lu, David Alvarez-Melis, Grace Huynh
Domain Adaptation Strong Generalization Optimal Transport Representation Space Adversarial Method Histopathology Datasets Invariant Learning Histopathology Foundation Model

February 24, 2023

GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations
Tobias Huber, Maximilian Demmler, Silvan Mertes, Matthew L. Olson, Elisabeth André
Reinforcement Learning Counterfactual Explanation Artificial Intelligence Model General Strategy Reinforcement Learning Agent Adversarial Method Reinforcement Learning Model Visual Counterfactual Explanation

February 15, 2023

Evaluating Trade-offs in Computer Vision Between Attribute Privacy, Fairness and Utility
William Paul, Philip Mathew, Fady Alajaji, Philippe Burlina
Computer Vision Procedural Fairness Task Utility Adversarial Method Attribute Inference Attack Attribute Privacy

February 14, 2023

Adversarial Path Planning for Optimal Camera Positioning
Gaia Carenini, Alexandre Duplessis
Near Optimality Visual Sensor Adversarial Method Camera Configuration Camera Placement Adversarial Planning

February 7, 2023

December 20, 2022

A survey on text generation using generative adversarial networks
Gustavo Henrique de Rosa, João Paulo Papa
Timely Survey Generative Adversarial Network Text Generation Adversarial Method

August 8, 2022

AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning
Tianxing Zhang, Hanzhou Wu, Xiaofeng Lu, Guangling Sun
Contrastive Learning Pre Trained Adversarial Perturbation Encoder Side Adversarial Method Adversarial Watermarking

July 8, 2022

Probing Classifiers are Unreliable for Concept Removal and Detection
Abhinav Kumar, Chenhao Tan, Amit Sharma
Data Detection Learned Representation Adversarial Method Concept Removal Sensitive Topic Non Textual

June 16, 2022

A Closer Look at Smoothness in Domain Adversarial Training
Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, R. Venkatesh Babu
Adversarial Training Adversarial Text Glance Annotation Adversarial Loss Domain Adversarial Adversarial Method Average Smoothness

June 15, 2022

Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
JoonHo Jang, Byeonghu Na, DongHyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon
Feature Alignment Domain Adversarial Adversarial Method Open Set Domain Adaptation

May 19, 2022

Focused Adversarial Attacks
Thomas Cilloni, Charles Walter, Charles Fleming
Adversarial Attack Adversarial Example Adversarial Method Gradient Based Attack Gradient Based Adversarial

February 28, 2022

Probing the Robustness of Trained Metrics for Conversational Dialogue Systems
Jan Deriu, Don Tuggener, Pius von Däniken, Mark Cieliebak
Reinforcement Learning Native Robustness Adversarial Method Supervised Metric

February 17, 2022

Gradient Based Activations for Accurate Bias-Free Learning
Vinod K Kurmi, Rishabh Sharma, Yash Vardhan Sharma, Vinay P. Namboodiri
Adversarial Learning Gradient Based Unbiased Learning Adversarial Method BIAS Detection Bias Reduction

February 9, 2022

Imitation Learning by State-Only Distribution Matching
Damian Boborzi, Christoph-Nikolas Straehle, Jens S. Buchner, Lars Mikelsons
Imitation Learning Policy Learning Soft Actor Critic Distribution Matching Adversarial Method Adversarial Imitation Learning

January 22, 2022

Inter-Semantic Domain Adversarial in Histopathological Images
Nicolas Dumas, Valentin Derangère, Laurent Arnould, Sylvain Ladoire, Louis-Oscar Morel, Nathan Vinçon
Histopathological Image Robust Deep Domain Adversarial Adversarial Method Shift Equivariance Data Shift

January 11, 2022

DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift
Luca Strazzera, Valentina Gori, Giacomo Veneri
Domain Adaptation Case Study Time Series Domain Shift Domain Invariant Representation Adversarial Method Virtual Sensor