Adversarial Data Collection

Adversarial data collection focuses on creating datasets designed to expose weaknesses in machine learning models, thereby improving their robustness and fairness. Current research emphasizes developing efficient methods for generating these adversarial examples, often employing generative adversarial networks (GANs) or gradient-based attacks tailored to specific model architectures (e.g., Graph Neural Networks, deep convolutional neural networks). This approach is significant because it addresses the limitations of relying solely on naturally occurring data, leading to more reliable and resilient models across various applications, including hate speech detection, license plate recognition, and assistive robotics.

Papers

July 5, 2024

Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding
Xincan Feng, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
Knowledge Graph Unified Framework NLP Field Negative Sampling Adversarial Data Collection Smoothing Algorithm

March 28, 2024

Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset
Janis Goldzycher, Paul Röttger, Gerold Schneider
Adversarial Example Hate Speech Critical Lesson Individual Annotator Adversarial Data Collection Adversarial Datasets

March 19, 2024

Electioneering the Network: Dynamic Multi-Step Adversarial Attacks for Community Canvassing
Saurabh Sharma, Ambuj SIngh
Network Programming Adversarial Text Gradient Based Attack Multi Step Adversarial Attack Adversarial Data Collection Multi Step Attack Medium Manipulation

March 1, 2024

Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks
Takayuki Osa, Tatsuya Harada
Deep Reinforcement Learning Multi Agent Reinforcement Learning Health Policy Recommendation Cooperative Behavior Mixed Autonomy Adversarial Data Collection Response Diversity Professional Caregiver

October 25, 2023

Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition
Bishal Shrestha, Griwan Khakurel, Kritika Simkhada, Badri Adhikari
Training Data Adversarial Image License Plate Adversarial Data Collection

May 30, 2023

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
Kejun Tang, Jiayu Zhai, Xiaoliang Wan, Chao Yang
Optimal Transport Average Approximation Deep Generative Model Minimax Optimization Reg PINNs Adversarial Data Collection

May 25, 2023

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Haotian Xue, Alexandre Araujo, Bin Hu, Yongxin Chen
Adversarial Sample Adversarial Loss Controllability Condition Digital STEALTH Metric Adversarial Data Collection Projected Gradient Descent

May 20, 2023

Dynamic Transformers Provide a False Sense of Efficiency
Yiming Chen, Simin Chen, Zexin Li, Wei Yang, Cong Liu, Robby T. Tan, Haizhou Li
Adversarial Attack High Efficiency Inference Efficiency False Sense Early Exit Adversarial Data Collection Multi Exit Dynamic Transformer

March 30, 2023

Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness
Timothy Redgrave, Colton Crum
Adversarial Attack Adversarial Robustness Adversarial Sample Mini Batch Adversarial Data Collection

March 28, 2023

Improving the Transferability of Adversarial Samples by Path-Augmented Method
Jianping Zhang, Jen-tse Huang, Wenxuan Wang, Yichen Li, Weibin Wu, Xiaosen Wang, Yuxin Su, Michael R. Lyu
Task Transferability Adversarial Sample Adversarial Noise Adversarial Data Collection Gradient Path Path Augmentation

March 15, 2023

Enhancing Unsupervised Audio Representation Learning via Adversarial Sample Generation
Yulin Pan, Xiangteng He, Biao Gong, Yuxin Peng, Yiliang Lv
Audio Representation Mel Spectrogram Audio Processing Spectrogram Based Adversarial Data Collection

March 6, 2023

Adversarial Sampling for Fairness Testing in Deep Neural Network
Tosin Ige, William Marfo, Justin Tonkinson, Sikiru Adewale, Bolanle Hafiz Matti
Deep Neural Network Adversarial Attack Adversarial Sample Adversarial Data Collection FAIrness Testing Adversarial Training Algorithm

June 29, 2022

longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan, Jifan Chen, Eunsol Choi, Gabriella Chronis, Anubrata Das, Katrin Erk, Matthew Lease, Junyi Jessy Li, Yating Wu, Kyle Mahowald
Adversarial Attack Inherent Interpretability Question Answering Model Historical Linguistics Systematic Approach Adversarial Data Collection Adversarial Question Error Prediction

June 28, 2022

Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop
Damian Y. Romero Diaz, Magdalena Anioł, John Culnan
Full Model Real Human High Quality Machine Reading Comprehension Adversarial Data Adversarial Data Collection Extractive Question Annotation Study Successful Adversarial Attack

June 9, 2022

Learning to generate imaginary tasks for improving generalization in meta-learning
Yichen Wu, Long-Kai Huang, Ying Wei
LeArning Abstract Strong Generalization Adversarial Loss Meta Training Real World Task Adversarial Data Collection

March 25, 2022

A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin
Generative Model Adversarial Training Adversarial Learning Unified Alignment Adversarial Data Collection Generative Power Contrastive Energy

January 1, 2022

Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words
Haoxu Wang, Yan Jia, Zeqing Zhao, Xuyang Wang, Junjie Wang, Ming Li
Adversarial Sample Wake Word Adversarial Data Collection Ambiguous Word

December 16, 2021

Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, Douwe Kiela
Full Model AI Assistant Adversarial Data Collection

November 20, 2021

Adversarial Sampling for Solving Differential Equations with Neural Networks
Kshitij Parwani, Pavlos Protopapas
Neural Network Differential Equation Neural Topic Architecture Search Adversarial Data Collection

November 8, 2021

Adversarial sampling of unknown and high-dimensional conditional distributions
Malik Hassanaly, Andrew Glaws, Karen Stengel, Ryan N. King
Generative Adversarial Network Conditional Distribution Adversarial Data Collection Order Moment

Adversarial Data Collection

Papers

Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding

Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset

Electioneering the Network: Dynamic Multi-Step Adversarial Attacks for Community Canvassing

Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks

Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability

Dynamic Transformers Provide a False Sense of Efficiency

Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness

Improving the Transferability of Adversarial Samples by Path-Augmented Method

Enhancing Unsupervised Audio Representation Learning via Adversarial Sample Generation

Adversarial Sampling for Fairness Testing in Deep Neural Network

longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks

Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop

Learning to generate imaginary tasks for improving generalization in meta-learning

A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training

Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words

Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants

Adversarial Sampling for Solving Differential Equations with Neural Networks

Adversarial sampling of unknown and high-dimensional conditional distributions