Roberta-LightGBM: A hybrid model of deep fake detection with pre-trained and binary classification
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Abstract
In May 2023, a fake image of an explosion near the Pentagon gained widespread social media traction. It dragged down US markets momentarily, perhaps marking the first time an artificial intelligence (AI)-generated image has affected the market. The fictitious image originally surfaced on Facebook and showed a large column of smoke that a Facebook user said was close to the US military headquarters in Virginia. In this research, we proposed Roberta by combining lightGBM to construct the Roberta-LightGBM technique framework. This paper aims to reduce tampered fake content in media with good accuracy and faster mechanisms by designing these two approaches to detecting fake content using a natural language model and a machine learning algorithm combined to develop the proposed work. Roberta's NLP model helps us train large datasets in minimum time, compared to traditional techniques like the BERT technique, which requires ten times larger datasets to be trained in a wide range of applications. LightGBM was used to identify the solution of a machine learning algorithm using a decision tree to involve binary classification to predict whether the retrieved data was real or fake. It improved the faster training speed in handling large datasets with high accuracy; memory usage was reduced, resulting in better accuracy. As a result of the analysis, the proposed framework achieves the goal of this research when compared to alternative techniques such as the XGBoost technique, the Roberta-LightGBM technique gives 95.36% accuracy, the overall computational time is 4.4 seconds, and the implementation of Roberta to get 92.17% efficiency is shown experimentally in this paper.
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