Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and DefensesData modelsTrainingTraining dataSecurityToxicologyUnsolicited e-mailServersAs machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of ...
AI and machine learning* models have two primary ingredients: training data and algorithms. Think of an algorithm as being like the engine of a car, and training data as the gasoline that gives the engine something to burn: data makes an AI model go. A data poisoning attack is like if ...
Artificial intelligence (AI) data poisoning is when an attacker manipulates the outputs of an AI or machine learning model by changing its training data. The attacker's goal in an AI data poisoning attack is to get the model to produce biased or dangerous results during inference. AI and mach...
Data or AI poisoning attacks are deliberate attempts to manipulate the training data of artificial intelligence and machine learning (ML) models to corrupt their behavior and elicit skewed, biased or harmful outputs. AI tools have seen increasingly widespread adoption since the public release ofChatGPT...
Data poisoning is a type of attack that involves tampering with and polluting a machine learning model's training data, impacting the model's ability to produce accurate predictions.
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training... M Goldblum,D Tsipras,C Xie,... - 《IEEE Transactions...
Adversarial machine learningLabel flippingData poisoningDeep learningFederated learning (FL) is an emerging paradigm for distributed training of large-scale deep neural networks in which participants' data remains on their own devices with only model updates being shared with a central server. However, ...
Data Poisoning Attacks in Machine Learning This is a preview of subscription content, log in via an institution to check access. Editor information Editors and Affiliations Center for Secure Information Systems, George Mason University, Fairfax, VA, USA Sushil Jajodia Università degli Studi di ...
“We don’t yet know of robust defenses against these attacks. We haven’t yet seen poisoning attacks on modern [machine learning] models in the wild, but it could be just a matter of time,” says Vitaly Shmatikov, a professor at Cornell University who studies AI model security and was ...
worksheets/0xbdd35bdd • NeurIPS 2017 Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model.2 Paper Code Stronger...