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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 376–400 of 492 papers

TitleStatusHype
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning—0
On the Effectiveness of Poisoning against Unsupervised Domain Adaptation—0
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models—0
On the Relevance of Byzantine Robust Optimization Against Data Poisoning—0
On the Robustness of Graph Reduction Against GNN Backdoor—0
A Study of Backdoors in Instruction Fine-tuned Language Models—0
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents—0
Optimizing ML Training with Metagradient Descent—0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models—0
OVLA: Neural Network Ownership Verification using Latent Watermarks—0
PACOL: Poisoning Attacks Against Continual Learners—0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning—0
Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks—0
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks—0
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning—0
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models—0
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey—0
Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data—0
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—0
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code—0
Policy Teaching via Data Poisoning in Learning from Human Preferences—0
Post-Training Overfitting Mitigation in DNN Classifiers—0
Practical Data Poisoning Attack against Next-Item Recommendation—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Practical Poisoning Attacks on Neural Networks—0
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