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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 226–250 of 492 papers

TitleStatusHype
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Clean Label Attacks against SLU Systems—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Computation and Data Efficient Backdoor Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers—0
ControlNET: A Firewall for RAG-based LLM System—0
Concealed Data Poisoning Attacks on NLP Models—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
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