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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

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Showing 51–75 of 492 papers

TitleStatusHype
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on Semi-Supervised Learning—0
Detection of Physiological Data Tampering Attacks with Quantum Machine Learning—0
SoK: Benchmarking Poisoning Attacks and Defenses in Federated LearningCode2
Safety at Scale: A Comprehensive Survey of Large Model SafetyCode3
TrojanTime: Backdoor Attacks on Time Series Classification—0
Provably effective detection of effective data poisoning attacks—0
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender SystemsCode0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
Cut the Deadwood Out: Post-Training Model Purification with Selective Module Substitution—0
Attacks on the neural network and defense methods—0
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning—0
From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security—0
One Pixel is All I Need—0
BiCert: A Bilinear Mixed Integer Programming Formulation for Precise Certified Bounds Against Data Poisoning Attacks—0
Deep Learning Model Security: Threats and Defenses—0
Learning to Forget using Hypernetworks—0
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Delta-Influence: Unlearning Poisons via Influence FunctionsCode0
Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization—0
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization—0
Learning from Convolution-based Unlearnable DatasetsCode0
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