Microfake: How small-scale deepfakes can undermine society
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
Advances in deepfake technology have led to the emergence of a new picture of how doctored material will be used in disinformation campaigns. While safeguards ensure that manipulated videos may not be such a problem at the highest levels of security and defence, lower levels – such as local elections – remain vulnerable to malign actors. At such levels, deepfakes can be distributed using social media channels to target unsuspecting victims. Current solutions only protect individuals who are prominent enough to be covered by the mainstream media, and not enough is being done by governments or social media companies to protect ordinary users from coordinated inauthentic activity online. However, with more images and videos of ourselves online than ever before, anyone can be a victim of a disinformation campaign. As deepfakes become easier to make, no one is safe – hyper-localized manipulation will create problems for democratic institutions that have not yet been fully understood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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