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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 251–275 of 492 papers

TitleStatusHype
Transferable Availability Poisoning AttacksCode0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor AttacksCode0
Towards Poisoning Fair Representations—0
Post-Training Overfitting Mitigation in DNN Classifiers—0
Seeing Is Not Always Believing: Invisible Collision Attack and Defence on Pre-Trained ModelsCode0
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning AttacksCode0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Systematic Testing of the Data-Poisoning Robustness of KNN—0
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy—0
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks—0
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?—0
On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks—0
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving—0
OVLA: Neural Network Ownership Verification using Latent Watermarks—0
Data Poisoning to Fake a Nash Equilibrium in Markov Games—0
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users—0
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization—0
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models—0
Differentially-Private Decision Trees and Provable Robustness to Data PoisoningCode0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate ModelsCode0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
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