SLIIT International Conference on Engineering and Technology [SICET]

Now showing 1 - 3 of 3

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Evaluating the Threshold of Authenticity in Deepfake Audio and Its Implications Within Criminal Justice
(SLIIT, Faculty of Engineering, 2024-10) Rodgers, J; Jones, K.O; Robinson, C; Chandler-Crnigoj, S; Burrell, H; McColl, S
Deepfake technology has come a long way in recent years and the world has already seen cases where it has been used maliciously. After a deepfake of UK independent financial advisor and poverty champion Martin Lewis was released on social media, a theory has been proposed where the deepfake target is accompanied by additional media to increase the authenticity of the file, for instance, ambient noise or processing to match how the deepfake would sound if it was recorded from a specific device such as a cellular/mobile phone. Focussing on deepfake audio, a critical listening experiment was conducted where participants were asked to identify the deepfake audio file from a set of three, across a number of sets of three files. A number of audio files were created using real voices with additional sounds added, volunteers recording their voice which is then put through a deepfake generation system, and voices taken from publicly available podcasts which were also applied to the deepfake software – the latter set mimics using web accessible voice recordings of prominent or famous people, such as the Prime Minister of the UK. The results show participants were able to successfully detect one third of the deepfake audio files presented, however they also incorrectly marked another one third of the real files as deepfakes whilst the remaining third were missed. Results also showed no definitive confirmation that audio and/or forensic professionals had any greater ability to successfully detect deepfake audio files when compared to others. The false positive result may also reinforce the scepticism and lack of trust created by what is known as “Liar’s Dividend”. The paper details how the files were created, the testing methodology, and the experimental results. Furthermore, a discussion on the future directions of research and the effects that deepfakes may have on the criminal justice system is presented.
Embargo
Enriching Resilience in Smart Manufacturing: Critical Analysis of Industry 4.0 Technologies on Sustainable Production Process
(SLIIT, Faculty of Engineering, 2024-10) Karunarathne, P L D N; Gamage, H C T; Kawya, M V T
Integration of Industry 4.0 technologies in smart manufacturing in the context of sustainable production processes has opened new paths to enrich resilience. This research aims at how Industry 4.0 technologies can be applied to smart manufacturing which is empowered by sustainable production processes in order to achieve maximum resilience. Through a broad literature review, key theories, concepts, and practical and theoretical studies related to smart manufacturing, resilience, and sustainability of production processes are examined. And the theme itself drives to establish a couple of research questions in order to find solutions and derive research inventions as to how Industry 4.0 technologies can enrich resilience in manufacturing and what sustainability implications arise from their adoption. And data gathering is been done using a simple questionnaire and analyzed using an extensive as well as a comparative method after considering how production processes have been changed before and after applying Industry 4.0 technologies. Overall, Industry 4.0 not only creates a more efficient and higher quality production process, but it also enables the things like predictive and preventive maintenance and upgrades, which results in lower downtime and less capital expenditure over time.
Open Access
Cryptocurrency Price Prediction: A Comparative Study using LSTM, GRU and Stacking Ensemble Algorithm for Time Series Forecasting
(SLIIT, 2022-02-11) Ashikul Islam, M. D
Technology has significantly reshaped how humans interact with their tangible and intangible surroundings. Cryptocurrency is considered to be one of the most recent technological inventions which revolutionized how we perceive currencies and their functionality. It has become popular because of its safety, security and anonymity. However, volatility remains one of the major issues with cryptocurrencies to this day. Therefore, the primary aim of this paper is to develop LSTM (Long ShortTerm Memory), GRU (Gated Recurrent Units) and a Stacking Ensemble Learning algorithm that efficiently predicts the price of a cryptocurrency for a given period of time. The predictions are then observed and analysed to determine the comparative performance of the said algorithms.

SLIIT International Conference on Engineering and Technology [SICET]

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