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As machine learning systems grow in scale, so do their training datarequirements, forcing practitioners to automate and outsource the curation oftraining data in order to achieve state-of-the-art performance. The absence oftrustworthy human supervision over the data collection process exposesorganizations to security vulnerabilities; training data can be manipulated tocontrol and degrade the downstream behaviors of learned models. The goal ofthis work is to systematically categorize and discuss a wide range of datasetvulnerabilities and exploits, approaches for defending against these threats,and an array of open problems in this space. In addition to describing variouspoisoning and backdoor threat models and the relationships among them, wedevelop their unified taxonomy.

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses