Legal Definitions of Intimate Images in the Age of Sexual Deepfakes and Generative AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores the evolution of Canadian criminal and civil responses to non-consensual synthetic intimate image creation and distribution. In recent years, the increasing accessibility of this type of technology, sometimes called deepfakes, has led to the proliferation of non-consensually created and distributed synthetic sexual images of both adults and minors. This is a form of image-based sexual abuse that law makers have sought to address through criminal child pornography laws and non-consensual distribution of intimate image provisions, as well as provincial civil intimate image legislation. Depending on the province a person resides in and the age of the person in the image, they may or may not have protection under existing laws. This article reviews the various language used to describe what is considered an intimate image, ranging from definitions seemingly limited to authentic intimate images to altered images and images that falsely present the person in a reasonably convincing manner.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it