The Real Threat of Deepfake Pornography: A Review of Canadian Policy
Bibliographic record
Abstract
Deepfakes may refer to algorithmically synthesized material wherein the face of a person is superimposed onto another body. To date, most deepfakes found online are pornographic, with the people depicted in them rarely consenting to their creation and publicization. Deepfakes leave anyone with an online presence vulnerable to victimization. As a testament to policy often being reactionary to antisocial behavior, current Canadian legislation offers no clear recourse to those who are victimized by deepfake pornography. We aim to provide a critical review of the legal mechanisms and remedies in place, including criminal charges, defamation, copyright infringement laws, and injunctive relief that could be applied in deepfake pornography cases. To combat deepfake pornography, we suggest current laws to be expanded to include language specific to falsely created pornography without the explicit consent of all depicted persons. We also discuss the extent to which host websites are responsible for vetting the uploaded content on their platforms. Finally, we present a call for action on a societal and research level to deal with deepfakes and better support victims of deepfake pornography.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".