Name
Abstract | ||
|---|---|---|
Age is a soft biometric trait that can aid law enforcement in the identification of victims of Child Sexual Exploitation Material (CSEM) creation/distribution. Accurate age estimation of subjects can classify explicit content possession as illegal during an investigation. Automation of this age classification has the potential to expedite content discovery and focus the investigation of digital evidence through the prioritisation of evidence containing CSEM. In recent years, artificial intelligence based approaches for automated age estimation have been created, and many public cloud service providers offer this service on their platforms. The accuracy of these algorithms have been improving over recent years. These existing approaches perform satisfactorily for adult subjects, but perform wholly inadequately for underage subjects. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.fsidi.2020.300921 | Forensic Science International: Digital Investigation |
Keywords | DocType | Volume |
Child Sexual Exploitation Material (CSEM),Age estimation,Underage facial age dataset,Child sexual abuse investigations,Deep learning | Journal | 32 |
Issue | ISSN | Citations |
S | 2666-2817 | 1 |
PageRank | References | Authors |
0.40 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felix Anda | 1 | 2 | 2.44 |
| Nhien-An Le-Khac | 2 | 224 | 49.63 |
| Mark Scanlon | 3 | 23 | 10.74 |