DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks
DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks
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发表时间：
2022
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通讯作者：
Jaron Mink;Licheng Luo;N. Barbosa;Olivia Figueira;Yang Wang;Gang Wang
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作者：
Jaron Mink;Licheng Luo;N. Barbosa;Olivia Figueira;Yang Wang;Gang Wang
来自深度学习模型（deepfakes）的虚构媒体最近已被应用于通过构建可信的社交角色来促进社会工程工作。虽然现有的工作主要集中在深度伪造检测上，但很少有人了解用户如何感知深度伪造角色并与之交互（例如，在社会工程的背景下。在本文中，我们进行了一项用户研究（n = 286），以定量评估deepfake伪影如何影响社交媒体个人资料的感知可信度以及个人资料与用户联系的可能性。我们的研究调查了单个媒体场（图像或文本）中孤立的伪影以及多个场之间的不匹配关系。我们还评估用户提示（或培训）是否使用户在此过程中受益。我们发现，人工制品和提示显着降低了deepfake配置文件的可信度和请求接受度。即便如此，用户仍然容易受到攻击，其中43%的用户在最佳情况下连接到deepfake配置文件。通过定性数据，我们发现了许多原因，为什么这项任务对用户来说是具有挑战性的，例如区分文本工件与诚实错误的困难，以及连接决策所带来的社会压力。最后，我们讨论了我们的结果对内容版主，社交媒体平台和未来防御的影响。
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 deep-fake 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.