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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 281–290 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
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