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

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Showing 201–250 of 492 papers

TitleStatusHype
One Pixel is All I Need—0
Data Poisoning to Fake a Nash Equilibrium in Markov Games—0
Online Data Poisoning Attack—0
Online Data Poisoning Attacks—0
On Optimal Learning Under Targeted Data Poisoning—0
On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks—0
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
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning—0
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning—0
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release—0
PrivacyGAN: robust generative image privacy—0
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models—0
Property Inference From Poisoning—0
Protecting against simultaneous data poisoning attacks—0
Protecting Proprietary Data: Poisoning for Secure Dataset Release—0
Provably effective detection of effective data poisoning attacks—0
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning—0
Proving Data-Poisoning Robustness in Decision Trees—0
Purifying Large Language Models by Ensembling a Small Language Model—0
QTrojan: A Circuit Backdoor Against Quantum Neural Networks—0
Reaching Data Confidentiality and Model Accountability on the CalTrain—0
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning—0
Redactor: A Data-centric and Individualized Defense Against Inference Attacks—0
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning—0
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation—0
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance—0
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