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Noelle Martin was the victim of a long-term campaign of non-consensual sexual imagery, which escalated from photoshopped stills to AI-generated deepfake pornographic videos. The incident highlights the severe psychological impact of image-based sexual abuse and the challenges in legal and platform-based enforcement. Martin's advocacy eventually contributed to the criminalization of image-based abuse in Australia.
In 2017, Noelle Martin discovered explicit deepfake videos online that used AI technology to superimpose her face onto pornographic scenes. This incident was a continuation of the abuse she had experienced since at least 2012, when she first found doctored still images of herself in similar contexts. Despite the initial lack of legal protections, her advocacy efforts were instrumental in making image-based abuse a criminal offense in Australia.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery.
Also classified as
The victim and online users were exposed to highly toxic, non-consensual pornographic deepfake videos generated using AI.
The incident involved the creation of humiliating and non-consensual sexual imagery targeting a specific victim to harass and degrade her.
Human
Due to a decision or action made by humans
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The harm was caused by human actors intentionally using deployed AI deepfake generation tools to target the victim with non-consensual pornographic videos.
Added categories peak at 4: Severe (Epistemic Harm, Inferred)
The incident involves the targeted creation of non-consensual AI-generated deepfake pornography against an Australian citizen and global celebrities. While causing severe psychological harm and highlighting regulatory gaps in managing synthetic media, its direct implications for national security remain minor, manageable through standard legal and domestic policy frameworks.
The report describes deepfake software and apps (like FaceApp and Zao) as user-friendly tools that allow individuals to input images and generate synthetic videos. These systems act as deterministic tools executing specific image-processing functions under direct human control.
Limited Risk: The report describes AI-generated content such as deepfakes, which are classified under Limited Risk and subject to transparency obligations.
The AI systems described (Face2Face, FaceApp, Zao) were primarily used to generate highly realistic, synthetic deepfake videos and images by mapping the victim's face onto other bodies.
The report is a detailed, first-person account of the victim's experience with deepfake technology, providing clear timelines, emotional impacts, and specific technological context (Face2Face, FaceApp, Zao). The facts regarding the AI's role as a tool for generating non-consensual sexual imagery are unambiguous.
None. The incident is a clear-cut case of targeted harassment and non-consensual deepfake pornography.
Technology
Accessible Deep Learning Software
Open-source and user-friendly apps allowed anyone to generate deepfakes.
Realistic Facial Reanimation
Systems like Face2Face enabled highly convincing facial manipulation.
Rapid AI Tech Proliferation
The rapid spread of deepfake algorithms outpaced safety barriers.
Data Inputs
Unprotected Social Media Photos
Perpetrators easily scraped personal photos uploaded to Facebook.
Abundance of Training Data
Large volumes of online images allowed AI to learn and replicate faces.
Lack of Image Provenance
No metadata or watermarks prevented unauthorized reuse of personal images.
Human Factors
Malicious Intent of Creators
Perpetrators targeted individuals for harassment and non-consensual porn.
Inherent Trust in Video Content
Audiences naturally trust video evidence, magnifying the impact of fakes.
Social Media Over-sharing
Users posted personal photos without anticipating malicious AI exploitation.
Process and Methods
Weak Platform Moderation
Websites and platforms failed to proactively detect and block deepfakes.
Ineffective Takedown Processes
Host sites ignored requests or allowed deleted content to be re-uploaded.
Lack of Host Accountability
Some site administrators extorted victims instead of removing content.
Management
Delayed Platform Bans
Reddit and Twitter delayed banning explicit deepfakes until 2018.
Prioritizing Growth over Safety
Platforms prioritized user growth over implementing robust safety safeguards.
Lack of Proactive Risk Assessment
Tech companies failed to foresee the weaponization of deep learning tech.
Regulatory Environment
Lagging Cyber Abuse Laws
Existing laws in 2012 lacked provisions for non-consensual image abuse.
Lack of Global Enforcement
Jurisdictional boundaries made it difficult to police international hosts.
Inadequate Platform Penalties
No regulatory penalties existed to force platforms to remove fake content.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
