SOTAVerified

Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 492 papers

TitleStatusHype
Learning from Convolution-based Unlearnable DatasetsCode0
Odyssey: Creation, Analysis and Detection of Trojan ModelsCode0
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite AggregationCode0
Indiscriminate Data Poisoning Attacks on Neural NetworksCode0
Depth-2 Neural Networks Under a Data-Poisoning AttackCode0
Defending Against Repetitive Backdoor Attacks on Semi-supervised Learning through Lens of Rate-Distortion-Perception Trade-offCode0
Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor AttacksCode0
Defending Distributed Classifiers Against Data Poisoning AttacksCode0
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning AttacksCode0
FullCert: Deterministic End-to-End Certification for Training and Inference of Neural NetworksCode0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion ModelsCode0
Game-Theoretic Unlearnable Example GeneratorCode0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
Fooling Partial Dependence via Data PoisoningCode0
Detecting AI Trojans Using Meta Neural AnalysisCode0
Generalization Bound and New Algorithm for Clean-Label Backdoor AttackCode0
On Adversarial Bias and the Robustness of Fair Machine LearningCode0
Training-free Lexical Backdoor Attacks on Language ModelsCode0
Data Poisoning Attack against Unsupervised Node Embedding Methods—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Data Poisoning against Differentially-Private Learners: Attacks and Defenses—0
Adversarial Vulnerability of Active Transfer Learning—0
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study—0
Data-Dependent Stability Analysis of Adversarial Training—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Cut the Deadwood Out: Post-Training Model Purification with Selective Module Substitution—0
Concealed Data Poisoning Attacks on NLP Models—0
Backdoor Attack and Defense for Deep Regression—0
ControlNET: A Firewall for RAG-based LLM System—0
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Computation and Data Efficient Backdoor Attacks—0
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Attacks on the neural network and defense methods—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Clean Label Attacks against SLU Systems—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Show:102550
← PrevPage 4 of 10Next →

No leaderboard results yet.