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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 101–150 of 492 papers

TitleStatusHype
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?Code0
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection—0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning—0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents—0
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR ImagesCode0
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift—0
A Geometric Approach to Problems in Optimization and Data Science—0
Investigating cybersecurity incidents using large language models in latest-generation wireless networks—0
ControlNET: A Firewall for RAG-based LLM System—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Optimizing ML Training with Metagradient Descent—0
Policy Teaching via Data Poisoning in Learning from Human Preferences—0
Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification—0
Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models—0
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models—0
Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data—0
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols—0
Approaching the Harm of Gradient Attacks While Only Flipping Labels—0
Atlas: A Framework for ML Lifecycle Provenance & Transparency—0
No, of course I can! Refusal Mechanisms Can Be Exploited Using Harmless Fine-Tuning Data—0
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL—0
Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs—0
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoningCode0
Multi-Faceted Studies on Data Poisoning can Advance LLM DevelopmentCode0
A Robust Attack: Displacement Backdoor Attack—0
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
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
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization—0
Learning from Convolution-based Unlearnable DatasetsCode0
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