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This study is a deep learning approach to classify images as either forged (fake) or pristine (original, not manipulated). The purpose of this study is to create a convolutional neural network that can recognize different types of manipulations on an image with an optimal accuracy. Some of these manipulations include plain white/black patches, filtered patches, and GAN-generated images. The CNN was created during my Data Science Internship at Factogram, a startup dedicated to the spread of fact-based information via social media. I was kindly given the chance to spearhead this project under the guidance of Factogram's founder, Tooraj Helmi: https://toorajhelmi.github.io/home

Mekhla Kapoor's Projects

neuronal-network-model-of-mouse-v1-circuitry icon neuronal-network-model-of-mouse-v1-circuitry

A realistic spiking neuronal network of 10,000 LIF neurons and LGN inputs built and simulated with the Allen Institute's Brain Modeling ToolKit (BMTK). Experiental data accessed through the AllenSDK from NWB files.

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