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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
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
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