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@article{207495,
        author = {Ankita Kaushal and Dr. Pankaj Garg and Dr. Ripudaman Singh and Dr. Ranjit Singh Chopra},
        title = {Human and AI-Generated Profile Pictures: A Comparative Study of Visual Identity Construction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1347-1355},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207495},
        abstract = {Profile pictures function as condensed identity interfaces: they precede interaction, organize first impressions, and often remain publicly visible when other profile information is restricted. Generative artificial intelligence has altered this function by enabling realistic faces that signify personhood without documenting an existing person. This chapter reports an original comparative secondary analysis of 24 empirical studies and datasets—15 examining human or edited profile photographs and nine examining AI-generated faces and synthetic profiles. Evidence units were coded for identity-signalling strength, direction of trust or engagement effects, and authenticity risk. Human and edited photographs produced a high mean identity-signal score (1.87/2) and positive trust or engagement effects in 47% of studies, but their mean authenticity-risk score remained moderate (0.67/2). AI-generated faces produced an equally high identity-signal score (1.89/2) and positive first-impression effects in 44% of studies, while receiving the maximum mean authenticity-risk score (2.00/2). Experimental evidence shows that synthetic faces can be indistinguishable from real faces, judged more trustworthy, and—in the case of White AI faces—perceived as more human than photographs of actual people. In-the-wild evidence simultaneously documents their use in scams, spam, coordinated amplification, and fabricated accounts. The chapter concludes that AI changes the profile picture from selective self-presentation into potentially synthetic identity establishment. It proposes the TRACE model—Transparency, Referential integrity, Audience-context fit, Consent and control, and Engagement accountability—for platform governance and responsible visual identity practice.},
        keywords = {Artificial Intelligence, Digital Identity, Identity Establishment, Online Engagement, Profile Pictures, Social Networking Sites.},
        month = {August},
        }