Comparing Methods for Detecting Child Exploitation Content Online
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
The sexual exploitation of children online is seen as a global issue and has been addressed by both governments and private organizations. Efforts thus far have focused primarily on the use of image hash value databases to find content. However, recently researchers have begun to use keywords as a way to detect child exploitation content. Within the current study we explore both of these methodologies. Using a custom designed web-crawler, we create three networks using the hash value method, keywords method, and a hybrid method combining the first two. Results first show that the three million images found in our hash value database were not common enough on public websites for the hash value method to produce meaningful result. Second, the small sample of websites that were found to contain those images had little to no videos posted, suggesting a need for different criteria for finding each type of material. Third, websites with code words commonly known to be used by child pornographers to identify or discuss exploitative content, were found to be much larger than others, with extensive visual and textual content. Finally, boy-centered keywords were more commonly found on child exploitation websites than girl-centered keywords, though not at a statistically significant level. Applications for law enforcement and areas for future research are discussed.
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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 | 0.001 | 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