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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 201–225 of 492 papers

TitleStatusHype
Fed-Credit: Robust Federated Learning with Credibility Management—0
SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search—0
On the Relevance of Byzantine Robust Optimization Against Data Poisoning—0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning—0
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-BackdoorsCode0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code—0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning—0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Purifying Large Language Models by Ensembling a Small Language Model—0
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms—0
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems—0
The Effect of Data Poisoning on Counterfactual ExplanationsCode0
Game-Theoretic Unlearnable Example GeneratorCode0
Security and Privacy Challenges of Large Language Models: A Survey—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning—0
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