Framing digital inauthenticity: Comparing user detection of AI-generated faces to messaged-based scam methods

Advancements in generative artificial intelligence (genAI) have made it easier to impersonate someone else, allowing users to create realistic images of entirely new personas or trusted individuals. While susceptibility to message-based inauthenticity (e.g., phishing) is well investigated, it remains unclear if there are similar cognitive mechanisms that support inauthenticity detection across message- and image-based techniques (e.g., AI-generated faces). The present study examined (1) if users are similar in their detection of inauthenticities that are image- or messaged-based, (2) if the same individual differences that predict susceptibility to message-based inauthenticity extend to image-based inauthenticity, and (3) if there are other individual differences that are broadly related to digital inauthenticity detection. For the message-based tasks, participants classified real and phishing emails/text messages. For the image-based task, participants classified real and AI-generated faces. Participants' cognitive reasoning styles, digital and risk literacy, and demographics were also assessed. Our findings suggest that users may be less likely to detect image-based digital inauthenticity, like AI-generated faces, compared to message-based scams, like phishing attacks. Additionally, our results indicate that while participants made poorer and riskier classifications with the faces, user characteristics such as risk literacy, cognitive reflection, and gender may be linked to the successful identification of image-based inauthenticity. Shared vulnerability across methods may depend on similarities in content, such as incorporating images/text. As online actors leverage genAI-tools to help develop more elaborate methods of digital inauthenticity, users will likely continue to struggle to identify digital inauthenticity, emphasizing the need for better technical safeguards and user interventions.