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AI incident #943 ·

Nonconsensual Explicit AI-Generated Images of Up to 60 Gladstone Park Secondary College Students in Victoria, Australia, Reportedly Created and Circulated Online

Open on the AI Incident Database 1 news report Synced from the AIID API · record last edited 5 Sep 2026

What happened

Purportedly AI-generated explicit images of up to 60 female students at Gladstone Park Secondary College in Victoria, Australia, were reportedly created and circulated online. The images were reportedly altered from formal school photos using AI tools to produce nonconsensual explicit content. Two Year 11 students have been suspended, but the full number of individuals involved remains unclear as the investigation is ongoing.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Police investigate sexually explicit deepfake images of students at Gladstone Park Secondary College - ABC News
    abc.net.au · Nate Woodall · AIID #4776

Who was involved

Alleged harmed party
Women and girls Up to 60 unnamed students at Gladstone Park Secondary College Students High school students Gladstone Park Secondary College students Gladstone Park Secondary College Girls Families of Gladstone Park Secondary College students Educational communities Adolescent girls
On AIID: Women and girls, Up to 60 unnamed students at Gladstone Park Secondary College, Students, High school students, Gladstone Park Secondary College students, Gladstone Park Secondary College, Girls, Families of Gladstone Park Secondary College students, Educational communities, Adolescent girls

AI systems implicated

Synthetic media generation technologySynthetic image generation technologyDeepfake technologyAI nudification tools

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
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Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 4.3.

  • Impersonation/identity theft

    "Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    "IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    "Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    "Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cybersecurity

    "LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Domain-Specific Misuses

    "Improvements in LLMs may exert greater pressure to apply LLMs to various domains, such as health and education (Eloundou et al., 2023). Crude efforts to use LLMs in such domains, however, may incur harm and should be di...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Linked by AIID editors or by its text-similarity model.

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