Adversarial Learning

Adversarial learning is a machine learning technique that improves model robustness and fairness by pitting two neural networks against each other—a generator and a discriminator—in a competitive training process. Current research focuses on applications across diverse fields, including improving fairness in predictive analytics, enhancing robustness in reinforcement learning and domain adaptation, and mitigating the effects of adversarial attacks on various models. This approach is significant because it addresses critical limitations of standard machine learning methods, leading to more reliable and equitable outcomes in various applications, from financial risk assessment to medical image analysis and autonomous systems.

Papers

December 2, 2024

Cross Domain Adaptation using Adversarial networks with Cyclic loss
Manpreet Kaur, Ankur Tomar, Srijan Mishra, Shashwat Verma
Domain Adaptation Adversarial Learning Deep Network Adversarial Setting Deep Learning Network Dynamic Loss

December 1, 2024

Well log data generation and imputation using sequence-based generative adversarial networks
Abdulrahman Al-Fakih, A. Koeshidayatullah, Tapan Mukerji, Sadam Al-Azani, SanLinn I. Kaka
Generative Adversarial Network Adversarial Learning Data Generation Imputation Algorithm State of the Art GAN Log Message Reservoir Model

November 28, 2024

November 24, 2024

ExAL: An Exploration Enhanced Adversarial Learning Algorithm
A Vinil, Aneesh Sreevallabh Chivukula, Pranav Chintareddy
Adversarial Attack Adversarial Perturbation Adversarial Learning Environment Exploration Model Resilience

November 21, 2024

Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians
William N. Caballero, Matthew LaRosa, Alexander Fisher, Vahid Tarokh
Adversarial Learning RSD Difference of Gaussian Conditional Distribution Gaussian Distribution Multivariate Approach Gray Box Disruptive Event Sequential Quadratic Programming Influence Diagram

November 15, 2024

Face De-identification: State-of-the-art Methods and Comparative Studies
Jingyi Cao, Xiangyi Chen, Bo Liu, Ming Ding, Rong Xie, Li Song, Zhu Li, Wenjun Zhang
Comparative Study Adversarial Learning State of the Art De Identification Face De Identification

November 14, 2024

Adversarial Attacks Using Differentiable Rendering: A Survey
Matthew Hull, Chao Zhang, Zsolt Kira, Duen Horng Chau
Timely Survey Adversarial Attack Adversarial Learning Differentiable Rendering

November 9, 2024

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
Shu Liu, Stanley Osher, Wuchen Li
Adversarial Learning Partial Differential Equation Krylov Subspace Training Network

November 8, 2024

Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model
Shuchang Lyu, Qi Zhaoa, Guangliang Cheng, Yiwei He, Zheng Zhou, Guangbiao Wang, Zhenwei Shi
Domain Adaptation Adversarial Learning Remote Sensing Adversarial Domain Adaptation Remote Human Operator Remote Sensing Image Semantic Segmentation UDA Algorithm

November 6, 2024

ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks
Ziji Shi, Jialin Li, Yang You
Generative Adversarial Network GAN Model Adversarial Learning Generative Question GAN Training Asynchronous Training

October 31, 2024

ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing
Haozhe Lei, Yunfei Ge, Quanyan Zhu
Adversarial Learning Neuro Symbolic Penetration Testing Critical Infrastructure Aligned Action Prompt

October 26, 2024

October 25, 2024

Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks
Zhengyang Lu, Tianhao Guo, Feng Wang
Semi Supervised Faithful Generation Adversarial Learning Semantic Alignment Classical Chinese Poetry

October 23, 2024

Advancing NLP Security by Leveraging LLMs as Adversarial Engines
Sudarshan Srinivasan, Maria Mahbub, Amir Sadovnik
Large Language Model Adversarial Attack Adversarial Learning Adversarial Natural Language

October 17, 2024

Adversarial Learning

Papers

Cross Domain Adaptation using Adversarial networks with Cyclic loss

Well log data generation and imputation using sequence-based generative adversarial networks

3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising

An Adversarial Learning Approach to Irregular Time-Series Forecasting

ExAL: An Exploration Enhanced Adversarial Learning Algorithm

Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians

Face De-identification: State-of-the-art Methods and Comparative Studies

Adversarial Attacks Using Differentiable Rendering: A Survey

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations

Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model

ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks

ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing

Classification under strategic adversary manipulation using pessimistic bilevel optimisation

Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification

Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks

Advancing NLP Security by Leveraging LLMs as Adversarial Engines

CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks

Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning

Diffusing States and Matching Scores: A New Framework for Imitation Learning

DurIAN-E 2: Duration Informed Attention Network with Adaptive Variational Autoencoder and Adversarial Learning for Expressive Text-to-Speech Synthesis