Phish Tycoon: phishing using voice cloning

Craig Albuquerque, Melwina Albuquerque

This project is a public service announcement highlighting the risks of voice cloning, an AI technology capable of creating synthetic voices nearly indistinguishable from real ones. The demo involves recording a user's voice during a phone call to generate a clone, which is then used in a simulated phishing call targeting the user's loved one.

Reviewer's Comments

Reviewer's Comments

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Lucas Hansen

Nice idea! Showing the entire workflow of audio collection→voice clone→scam phone call is quite helpful. I do recommend using better TTS and voice cloning. As is, the clone doesn’t really sound like you and the robotic TTS decreases the impact of the demo.

Épiphanie Gédéon

I really liked it. I would suggest using a better TTS during the conversational part, as this would have allowed for better immersion and to project what it could look like to have a conversation with someone over the phone only to have your voice stolen.The voice cloning was quite impressive although a bit off. With some polish I feel like this demo could really shine.

Adam Binksmith

Fantastic project! This is a near-term risk, presented in a visceral way! I love the conciseness of the website, and the reveal of the generated audio message. Next steps for this project would be polish - you could use a better voice for the AI conversation (e.g. an ElevenLabs voice), the voice cloning could maybe use additional tuning too, and with more time you could improve the website presentation overall. Great work on this!

Cite this work

@misc {

title={

@misc {

},

author={

Craig Albuquerque, Melwina Albuquerque

},

date={

8/26/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.