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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 276–300 of 492 papers

TitleStatusHype
On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based ApproachCode0
Defend Data Poisoning Attacks on Voice Authentication—0
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning—0
Do-AIQ: A Design-of-Experiment Approach to Quality Evaluation of AI Mislabel Detection Algorithm—0
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System—0
Neural network fragile watermarking with no model performance degradation—0
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning AttacksCode1
Lethal Dose Conjecture on Data PoisoningCode0
Testing the Robustness of Learned Index StructuresCode0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Backdoor Attacks on Crowd CountingCode1
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain—0
Backdoor Attack is a Devil in Federated GAN-based Medical Image SynthesisCode0
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
Autoregressive Perturbations for Data PoisoningCode1
Efficient Reward Poisoning Attacks on Online Deep Reinforcement LearningCode0
BagFlip: A Certified Defense against Data PoisoningCode0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
VPN: Verification of Poisoning in Neural Networks—0
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning—0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV—0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy—0
Indiscriminate Data Poisoning Attacks on Neural NetworksCode0
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