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 301–325 of 492 papers

TitleStatusHype
Temporal Robustness against Data Poisoning—0
The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures—0
The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline—0
Data Poisoning Attack against Knowledge Graph Embedding—0
Towards Multi-Objective Statistically Fair Federated Learning—0
Towards Poisoning Fair Representations—0
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization—0
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning—0
Training set cleansing of backdoor poisoning by self-supervised representation learning—0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning—0
Model-Agnostic Explanations using Minimal Forcing Subsets—0
TrojanTime: Backdoor Attacks on Time Series Classification—0
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning—0
Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems—0
Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training—0
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks—0
Understanding Influence Functions and Datamodels via Harmonic Analysis—0
Unlearnable Examples Detection via Iterative Filtering—0
UTrace: Poisoning Forensics for Private Collaborative Learning—0
VPN: Verification of Poisoning in Neural Networks—0
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift—0
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?—0
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning—0
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