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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 276–300 of 492 papers

TitleStatusHype
Shapley Homology: Topological Analysis of Sample Influence for Neural Networks—0
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks—0
Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models—0
Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning—0
Sniper GMMs: Structured Gaussian mixtures poison ML on large n small p data with high efficacy—0
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks—0
Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms—0
Spectrum Data Poisoning with Adversarial Deep Learning—0
Sself: Robust Federated Learning against Stragglers and Adversaries—0
SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection—0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce—0
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms—0
Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs—0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning—0
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers—0
Systematic Testing of the Data-Poisoning Robustness of KNN—0
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation—0
Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification—0
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning—0
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
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