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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 76–100 of 492 papers

TitleStatusHype
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP ModelsCode1
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning AttacksCode1
Stronger Data Poisoning Attacks Break Data Sanitization DefensesCode1
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive LearningCode1
Adversarial Vulnerability of Active Transfer Learning—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks—0
Backdoor Attack and Defense for Deep Regression—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Computation and Data Efficient Backdoor Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Attacks on the neural network and defense methods—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection—0
Model Hijacking Attack in Federated Learning—0
Atlas: A Framework for ML Lifecycle Provenance & Transparency—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
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