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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 492 papers

TitleStatusHype
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Learning and Unlearning of Fabricated Knowledge in Language Models—0
Learning to Forget using Hypernetworks—0
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning—0
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest—0
Mendata: A Framework to Purify Manipulated Training Data—0
Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification—0
Mitigating Data Poisoning in Text Classification with Differential Privacy—0
Mitigating the Impact of Adversarial Attacks in Very Deep Networks—0
Mixed Strategy Game Model Against Data Poisoning Attacks—0
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Multi-Trigger Poisoning Amplifies Backdoor Vulnerabilities in LLMs—0
Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective—0
Neural network fragile watermarking with no model performance degradation—0
Neuromimetic metaplasticity for adaptive continual learning—0
No, of course I can! Refusal Mechanisms Can Be Exploited Using Harmless Fine-Tuning Data—0
Reclaiming "Open AI" -- AI Model Serving Can Be Open Access, Yet Monetizable and Loyal—0
On Defending Against Label Flipping Attacks on Malware Detection Systems—0
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
Show:102550
← PrevPage 8 of 10Next →

No leaderboard results yet.