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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 126–150 of 492 papers

TitleStatusHype
Exploring Vulnerabilities and Protections in Large Language Models: A Survey—0
PureEBM: Universal Poison Purification via Mid-Run Dynamics of Energy-Based ModelsCode1
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model DynamicsCode1
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew ResilienceCode1
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
Fed-Credit: Robust Federated Learning with Credibility Management—0
SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search—0
On the Relevance of Byzantine Robust Optimization Against Data Poisoning—0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning—0
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-BackdoorsCode0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Optimistic Verifiable Training by Controlling Hardware NondeterminismCode1
Don't Forget What I did?: Assessing Client Contributions in Federated Learning—0
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code—0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
Learning to Poison Large Language Models for Downstream ManipulationCode1
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