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

March 31, 2023

Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning
Amit Kumar, Ajay Pratap, Anil Kumar Singh
Generative Adversarial Network Neural Machine Translation Adversarial Learning Multilingual Neural Machine Translation Low Resource Neural Machine Translation

March 28, 2023

fRegGAN with K-space Loss Regularization for Medical Image Translation
Ivo M. Baltruschat, Felix Kreis, Alexander Hoelscher, Melanie Dohmen, Matthias Lenga
Generative Adversarial Network Adversarial Learning Image to Image Translation Image Translation Regularization Loss Medical Image Translation

March 25, 2023

No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning
Thorsten Eisenhofer, Erwin Quiring, Jonas Möller, Doreen Riepel, Thorsten Holz, Konrad Rieck
Adversarial Learning Text Review Reviewer Assignment Benign Input

March 20, 2023

DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Shoumik Saha, Wenxiao Wang, Yigitcan Kaya, Soheil Feizi, Tudor Dumitras
Adversarial Learning Malware Detection Randomized Smoothing Certified Robustness Smoothed Classifier Malware Classifier Decentralized Riemannian

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 7, 2023

FIT: Frequency-based Image Translation for Domain Adaptive Object Detection
Siqi Zhang, Lu Zhang, Zhiyong Liu, Hangtao Feng
Generative Adversarial Network Adversarial Learning Image Translation Domain Adversarial Domain Adaptive Object Detection Adaptive Object Detection

March 6, 2023

ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning
Animesh Basak Chowdhury, Lilas Alrahis, Luca Collini, Johann Knechtel, Ramesh Karri, Siddharth Garg, Ozgur Sinanoglu, Benjamin Tan
Adversarial Learning Logic Synthesis Recipe Generation

March 3, 2023

February 25, 2023

Chaotic Variational Auto encoder-based Adversarial Machine Learning
Pavan Venkata Sainadh Reddy, Yelleti Vivek, Gopi Pranay, Vadlamani Ravi
Variational Autoencoder Adversarial Learning

February 21, 2023

A3S: Adversarial learning of semantic representations for Scene-Text Spotting
Masato Fujitake
Adversarial Learning Natural Image Text Recognition Semantic Representation Scene Text Scene Text Spotting

February 19, 2023

Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective
Baoyuan Wu, Zihao Zhu, Li Liu, Qingshan Liu, Zhaofeng He, Siwei Lyu
Adversarial Attack Adversarial Learning Comprehensive Survey New Attack Attack Paradigm Life Cycle Adversarial Influence

February 18, 2023

February 16, 2023

On the Effect of Adversarial Training Against Invariance-based Adversarial Examples
Roland Rauter, Martin Nocker, Florian Merkle, Pascal Schöttle
Adversarial Example Adversarial Training Mixed Effect Adversarial Learning

February 10, 2023

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making
Adam Block, Alexander Rakhlin, Max Simchowitz
Adversarial Learning Computational Complexity Online Prediction Piecewise Polynomial Smoothed Online Learning

February 9, 2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
Native Robustness Differential Privacy Adversarial Learning Distributed Learning Utility Privacy Trade Robust Aggregation Rule

February 3, 2023

Adversarial Learning

Papers

Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning

fRegGAN with K-space Loss Regularization for Medical Image Translation

No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning

DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness

Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review

FIT: Frequency-based Image Translation for Domain Adaptive Object Detection

ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning

Multi-Agent Adversarial Training Using Diffusion Learning

AdvART: Adversarial Art for Camouflaged Object Detection Attacks

Chaotic Variational Auto encoder-based Adversarial Machine Learning

A3S: Adversarial learning of semantic representations for Scene-Text Spotting

Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective

Redes Generativas Adversarias (GAN) Fundamentos Te\'oricos y Aplicaciones

StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

On the Effect of Adversarial Training Against Invariance-based Adversarial Examples

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks

Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation

Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation