SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 431–440 of 492 papers

TitleStatusHype
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm—0
Depth-2 Neural Networks Under a Data-Poisoning AttackCode0
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers—0
Data Poisoning Attacks on Federated Machine Learning—0
Practical Data Poisoning Attack against Next-Item Recommendation—0
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks—0
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM—0
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation—0
Defending against Backdoor Attack on Deep Neural Networks—0
Influence Function based Data Poisoning Attacks to Top-N Recommender Systems—0
Show:102550
← PrevPage 44 of 50Next →

No leaderboard results yet.