Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Computer Engineering
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Dissertations
Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …
Evaluating The Performance Impact Of Fine-Tuning Optimization Strategies On Pre-Trained Distilbert Models Towards Hate Speech Detection In Social Media, Aidan Mcgovern
Dissertations
Hate speech can be defined as forms of expression that incite hatred or encourage violence towards a person or group based on race, religion, gender, or sexual orientation. Hate speech has gravitated towards social media as its primary platform, and its propagation represents profound risks to both the mental well-being and physical safety of targeted groups. Countermeasures to moderate hate speech face challenges due to the volumes of data generated in social media, leading companies, and the research community to evaluate methods to automate its detection. The emergence of BERT and other pre-trained transformer-based models for transfer learning in the …
Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone
Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone
Dissertations
The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO) …
Investigating Effect Of Amount Of Augmented Data On Performance Of Convolutional Neural Network For Multiclass Image Classification, Shivam Khandelwal
Investigating Effect Of Amount Of Augmented Data On Performance Of Convolutional Neural Network For Multiclass Image Classification, Shivam Khandelwal
Dissertations
This research project seeks to investigate the use of Image Data augmentation that generates synthetic data by adding distortions to original images, as a means of replacement to a large amount of real data used to train the Convolutional Neural Networks. The purpose of the research project is to assess the effectiveness of augmented data over the real data by comparing the performance of the model trained with various amounts of augmented training and validation data ratio. Deep learning tasks involving convolutional neural networks have difficulty in generalizing the models effectively for computer vision tasks when the training dataset is …