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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 141–150 of 492 papers

TitleStatusHype
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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