Location
Additional Presentations- Allen E. Paulson College of Engineering and Computing
Document Type and Release Option
Thesis Presentation (Restricted to Georgia Southern)
Faculty Mentor
Hayden Wimmer
Faculty Mentor Email
hwimmer@georgiasouthern.edu
Presentation Year
2021
Start Date
26-4-2021 12:00 AM
End Date
30-4-2021 12:00 AM
Keywords
Deep learning, Artificial intelligence, Networks
Description
Deep learning is an Artificial Intelligent (AI) function that mimics the workings of the human brain in processing data such as speech recognition, visual object recognition, object detection, language translation, and making decisions. Deepfakes are images generated by artificial intelligence techniques where a person in an existing image or video is replaced by someone else’s likeness. A generative adversarial network (GAN) is a specific deep learning technique designed by Goodfellow et al. (2014) which generates new data from a given training set. This generates a new image which is referred to as a deepfake. In this work we developed deepfakes based on the public MNIST dataset using GAN. Deepfakes have become a societal challenge as they are difficult to impossible to distinguish from an authentic image. Future work will examine the accuracy of human subjects detecting deepfakes from authentic images.
Academic Unit
Allen E. Paulson College of Engineering and Computing
Generating Deepfakes from MNIST Dataset in Python, Keras and Tensorflow
Additional Presentations- Allen E. Paulson College of Engineering and Computing
Deep learning is an Artificial Intelligent (AI) function that mimics the workings of the human brain in processing data such as speech recognition, visual object recognition, object detection, language translation, and making decisions. Deepfakes are images generated by artificial intelligence techniques where a person in an existing image or video is replaced by someone else’s likeness. A generative adversarial network (GAN) is a specific deep learning technique designed by Goodfellow et al. (2014) which generates new data from a given training set. This generates a new image which is referred to as a deepfake. In this work we developed deepfakes based on the public MNIST dataset using GAN. Deepfakes have become a societal challenge as they are difficult to impossible to distinguish from an authentic image. Future work will examine the accuracy of human subjects detecting deepfakes from authentic images.
Comments
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