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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 21–30 of 492 papers

TitleStatusHype
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?Code0
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection—0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning—0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents—0
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR ImagesCode0
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift—0
A Geometric Approach to Problems in Optimization and Data Science—0
Investigating cybersecurity incidents using large language models in latest-generation wireless networks—0
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