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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 151–200 of 492 papers

TitleStatusHype
Reclaiming "Open AI" -- AI Model Serving Can Be Open Access, Yet Monetizable and Loyal—0
Learning and Unlearning of Fabricated Knowledge in Language Models—0
Inverting Gradient Attacks Makes Powerful Data Poisoning—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Regularized Robustly Reliable Learners and Instance Targeted Attacks—0
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning—0
Fragile Giants: Understanding the Susceptibility of Models to Subpopulation Attacks—0
Data Taggants: Dataset Ownership Verification via Harmless Targeted Data Poisoning—0
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning—0
Empirical Perturbation Analysis of Linear System Solvers from a Data Poisoning Perspective—0
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce—0
Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks—0
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks—0
UTrace: Poisoning Forensics for Private Collaborative Learning—0
Clean Label Attacks against SLU Systems—0
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using JailbreakingCode0
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers—0
Blockchain-based Federated Recommendation with Incentive Mechanism—0
Protecting against simultaneous data poisoning attacks—0
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender SystemsCode0
Unlearnable Examples Detection via Iterative Filtering—0
Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms—0
2D-OOB: Attributing Data Contribution Through Joint Valuation FrameworkCode0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense—0
Model Hijacking Attack in Federated Learning—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks—0
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor AttacksCode0
Defending Against Repetitive Backdoor Attacks on Semi-supervised Learning through Lens of Rate-Distortion-Perception Trade-offCode0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning—0
Robust Yet Efficient Conformal Prediction SetsCode0
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation—0
Neuromimetic metaplasticity for adaptive continual learning—0
If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers—0
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning—0
Securing Multi-turn Conversational Language Models From Distributed Backdoor TriggersCode0
On the Robustness of Graph Reduction Against GNN Backdoor—0
Machine Unlearning Fails to Remove Data Poisoning AttacksCode0
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning—0
FullCert: Deterministic End-to-End Certification for Training and Inference of Neural NetworksCode0
Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition—0
A Study of Backdoors in Instruction Fine-tuned Language Models—0
Certified Robustness to Data Poisoning in Gradient-Based TrainingCode0
Generalization Bound and New Algorithm for Clean-Label Backdoor AttackCode0
Exploring Vulnerabilities and Protections in Large Language Models: A Survey—0
Mitigating Backdoor Attack by Injecting Proactive Defensive BackdoorCode0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
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