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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 76–100 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
PoisonBench: Assessing Large Language Model Vulnerability to Data PoisoningCode1
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
UTrace: Poisoning Forensics for Private Collaborative Learning—0
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks—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
BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language ModelsCode3
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
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