Akademik Veri Yönetim Sistemi
Diğer, ss.1-30, 2024
This
paper presents a method to protect learning AI models against data and label
poisoning attacks; The Norm Culture method posits that each class in an image
classification problem possesses an inherent structure that serves as a primary
defense against attacks—such as data or label poisoning—that can corrupt new
training and testing samples during the parameter update phase of an AI
predictive model within a conventional deep learning framework. The method
requires calculating three elements from the essential training and testing
samples. The first element is the flattened matrix representing the class
image. The second element is the class alpha, a scalar that represents the
weight norm of the class. The final element is the most recently validated AI
predictive model. The experimental outcomes on a
binary class image classification dataset from health domains indicate that the
proposed method effectively identifies training and testing sample images
compromised by either type of attack one or two. Additionally, there is potential for enhancing the method
within the mathematical functions of the AI framework.
Keywords: Norm Culture Method, Classification, AI Attacks, training data poisoning and label poisoning