Child Abuse & Neglect
Automated detection of child sexual abuse images (CSAI) often relies on image attributes, such as hash values. However, electronic service providers and others without access to hash value databases are limited in their ability to detect CSAI. Additionally, the increasing amount of CSA content being distributed means that a large percentage of images are not yet cataloged in hash value databases. Therefore, additional detection criteria need to be determined to improve identification of non-hashed CSAI.
We aim to identify patterns in the locations and folder/file naming practices of websites hosting and displaying CSAI, to use as additional detection criteria for non-hashed CSAI.
Using a custom-designed web crawler and snowball sampling, we analyzed the locations and naming practices of 103 Surface Web websites hosting and/or displaying 8108 known CSAI hash values.
Websites specialize in either hosting or displaying CSAI with only 20% doing both. Neither hosting nor displaying websites fear repercussions. Over 27% of CSAI were displayed in the home directory (i.e., main page) with only 6% located in at least 4th-level sub-folder. Websites focused more on organizing images than hiding them with 68% of hosted and 54% of displayed CSAI being found in folders formatted year/month. Qualitatively, hosting websites were likely to use alphanumeric or disguised folder and file names to conceal images, while displaying websites were more explicit.
File and folder naming patterns can be combined with existing criteria to improve automated detection of websites and website locations likely hosting and/or displaying CSAI.
Child sexual exploitation images, Child sexual abuse images, Child pornography, Limitation of hash values, Websites, Automated data collection
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Enrique Guerra and Bryce G. Westlake. "Detecting child sexual abuse images: Traits of child sexual exploitation hosting and displaying websites" Child Abuse & Neglect (2021). https://doi.org/10.1016/j.chiabu.2021.105336