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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 101–125 of 492 papers

TitleStatusHype
Data Poisoning in LLMs: Jailbreak-Tuning and Scaling LawsCode3
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
A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future TrendsCode4
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
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