Humans unable to detect over a quarter of deepfake speech samples
University College London
Deepfakes are synthetic media intended to
resemble a real person's voice or appearance. They fall under the category of
generative artificial intelligence (AI), a type of machine learning (ML) that
trains an algorithm to learn the patterns and characteristics of a dataset,
such as video or audio of a real person, so that it can reproduce original
sound or imagery.
While early deepfake speech algorithms may
have required thousands of samples of a person's voice to be able to generate
original audio, the latest pre-trained algorithms can recreate a person's voice
using just a three-second clip of them speaking. Open-source
algorithms are freely available and while some expertise would be beneficial,
it would be feasible for an individual to train them within a few days.
Tech firm Apple recently announced software
for iPhone and iPad that allows a user to create a copy of their voice using 15
minutes of recordings.
Researchers at UCL used a text-to-speech (TTS) algorithm trained on two publicly available datasets, one in English and one in Mandarin, to generate 50 deepfake speech samples in each language. These samples were different from the ones used to train the algorithm to avoid the possibility of it reproducing the original input.
These artificially generated samples and
genuine samples were played for 529 participants to see whether they could
detect the real thing from fake speech. Participants were only able to identify
fake speech 73% of the time, which improved only slightly after they received
training to recognise aspects of deepfake speech.
Kimberly Mai (UCL Computer Science), first
author of the study, said: "Our findings confirm that humans are unable to
reliably detect deepfake speech, whether or not they have received training to
help them spot artificial content. It's also worth noting that the samples that
we used in this study were created with algorithms that are relatively old,
which raises the question whether humans would be less able to detect deepfake
speech created using the most sophisticated technology available now and in the
future."
The next step for the researchers is to
develop better automated speech detectors as part of ongoing efforts to create
detection capabilities to counter the threat of artificially generated audio
and imagery.
Though there are benefits from generative
AI audio technology, such as greater accessibility for those whose speech may
be limited or who may lose their voice due to illness, there are growing fears
that such technology could be used by criminals and nation states to cause
significant harm to individuals and societies.
Documented cases of deepfake speech being
used by criminals include one 2019 incident where the CEO of a British energy
company was convinced to transfer hundreds of thousands of pounds to a false
supplier by a deepfake recording of his boss's voice.
Professor Lewis Griffin (UCL Computer Science), senior author of the study, said: "With generative artificial intelligence technology getting more sophisticated and many of these tools openly available, we're on the verge of seeing numerous benefits as well as risks. It would be prudent for governments and organizations to develop strategies to deal with abuse of these tools, certainly, but we should also recognize the positive possibilities that are on the horizon."
