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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 331–340 of 492 papers

TitleStatusHype
DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
Backdoor Attack on Hash-based Image Retrieval via Clean-label Data PoisoningCode1
Backdoor Attack and Defense for Deep Regression—0
Excess Capacity and Backdoor PoisoningCode0
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
Certifiers Make Neural Networks Vulnerable to Availability Attacks—0
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
Classification Auto-Encoder based Detector against Diverse Data Poisoning AttacksCode0
Poison Ink: Robust and Invisible Backdoor AttackCode1
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