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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 351–375 of 492 papers

TitleStatusHype
Get a Model! Model Hijacking Attack Against Machine Learning Models—0
Mitigating Data Poisoning in Text Classification with Differential Privacy—0
CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data PoisoningCode0
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations—0
Protecting Proprietary Data: Poisoning for Secure Dataset Release—0
Backdoor Attack and Defense for Deep Regression—0
Excess Capacity and Backdoor PoisoningCode0
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
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles—0
Derivative-free Alternating Projection Algorithms for General Nonconvex-Concave Minimax Problems—0
Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoningCode0
Fairness-aware Summarization for Justified Decision-Making—0
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoningCode0
Understanding the Limits of Unsupervised Domain Adaptation via Data PoisoningCode0
Poisoning Attack against Estimating from Pairwise ComparisonsCode0
On the Effectiveness of Poisoning against Unsupervised Domain Adaptation—0
Data Poisoning Won’t Save You From Facial Recognition—0
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—0
Gradient-based Data Subversion Attack Against Binary Classifiers—0
A Gradient Method for Multilevel Optimization—0
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