DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks


Jaron Mink, Licheng Luo, and Natã M. Barbosa, University of Illinois at Urbana-Champaign; Olivia Figueira, Santa Clara University; Yang Wang and Gang Wang, University of Illinois at Urbana-Champaign


Fabricated media from deep learning models, or deepfakes, have been recently applied to facilitate social engineering efforts by constructing a trusted social persona. While existing works are primarily focused on deepfake detection, little is done to understand how users perceive and interact with deepfake persona (e.g., profiles) in a social engineering context. In this paper, we conduct a user study (n=286) to quantitatively evaluate how deepfake artifacts affect the perceived trustworthiness of a social media profile and the profile's likelihood to connect with users. Our study investigates artifacts isolated within a single media field (images or text) as well as mismatched relations between multiple fields. We also evaluate whether user prompting (or training) benefits users in this process. We find that artifacts and prompting significantly decrease the trustworthiness and request acceptance of deepfake profiles. Even so, users still appear vulnerable with 43% of them connecting to a deepfake profile under the best-case conditions. Through qualitative data, we find numerous reasons why this task is challenging for users, such as the difficulty of distinguishing text artifacts from honest mistakes and the social pressures entailed in the connection decisions. We conclude by discussing the implications of our results for content moderators, social media platforms, and future defenses.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {279946,
author = {Jaron Mink and Licheng Luo and Nat{\~a} M. Barbosa and Olivia Figueira and Yang Wang and Gang Wang},
title = {{DeepPhish}: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
isbn = {978-1-939133-31-1},
address = {Boston, MA},
pages = {1669--1686},
url = {},
publisher = {USENIX Association},
month = aug

Presentation Video