Browsing by Author "Burrell, H"

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Embargo
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.
Open Access
How Frequency and Harmonic Profiling of a ‘Voice’ Can Inform Authentication of Deepfake Audio: An Efficiency Investigation
(SLIIT, Faculty of Engineering, 2025-01) Williams, E.L; Jones, K.O; Robinson, J.C; handler-Crnigoj, S; Burrell, H; McColl, S
As life in the digital era becomes more complex, the capacity for criminal activity within the digital realm becomes even more widespread. More recently, the development of deepfake media generation powered by Artificial Intelligence pushes audio and video content into a realm of doubt, misinformation, or misrepresentation. The instances of deepfake videos are numerous, with some infamous cases ranging from manufactured graphic images of the musician Taylor Swift, through to the loss of $25 million dollars transferred after a faked video call. The problems of deepfake are becoming increasingly concerning for the general public when such material is submitted into evidence in a court case, especially a criminal trial. The current methods of authentication against such deepfake evidence threats are insufficient. When considering speech within audio forensics, there is sufficient ‘individuality’ in one’s own voice to enable comparison for identification. In the case of authenticating audio for deepfake speech, it is possible to use this same comparative approach to identify rogue or incomparable harmonic and formant patterns within the speech. The presence of deepfake media within the realms of illegal activity demands appropriate legal enforcement, resulting in a requirement for robust detection methods. The work presented in this paper proposes a robust technique for identifying such AI-synthesized speech using a quantifiable method that proves to be justified within court proceedings. Furthermore, it presents the correlation between the harmonic content of human speech patterns and the AI-generated clones they produce. This paper details which spectrographic audio characteristics were found that may prove helpful towards authenticating speech for forensic purposes in the future. The results demonstrate that using specific frequency ranges to compare against a known audio sample of a person’s speech, indicates the presence of deepfake media due to different harmonic structures.