Global ETD Search
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Showing 1 to 2 of 2 for “"security in Machine learning"”.
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A Systems Theory Approach to Cybersecuring a Supervised Machine Learning System
Machine learning is a rapidly growing field with many applications in areas such as healthcare, finance, and transportation. As machine learning becomes more prevalent, it is important to ensure that these systems are secure and can resist attacks from malicious actors. This is particularly …
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NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images
Recent advancements in generative models have resulted in the improvement of hyper- realistic synthetic images or "deepfakes" at high resolutions, making them almost indistin- guishable from real images from cameras. While exciting, this technology introduces room for abuse. Deepfakes have already …