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Increasing The Robustness Of Machine Learning By Adversarial Attacks, Gourab Mukhopadhyay
Increasing The Robustness Of Machine Learning By Adversarial Attacks, Gourab Mukhopadhyay
Theses and Dissertations
By perturbation or physical attacks any machine can be fooled into predicting something else other than the intended output. There are training data based on which the model is trained to predict unknown things. The objective was to create noises and shades of different levels on the images and do experiments for measuring accuracy and making the model classify the traffic signs. When it comes to adding shades to the pictures, pixels were modified for three different layers of the pictures. The experiment also shows that with the shadows getting deeper, the accuracies drop significantly. Here, some changes in pixels …
Evaluating Machine Learning Techniques For Smart Home Device Classification, Angelito E. Aragon Jr.
Evaluating Machine Learning Techniques For Smart Home Device Classification, Angelito E. Aragon Jr.
Theses and Dissertations
Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, …