Comparison of deep learning classification models for facial image age estimation in digital forensic investigations

There has been a significant rise in digital forensic investigations containing Indecent Images of Children (IIoC), and one of the major challenges faced by investigators is the time-consuming task of manually investigating images for illicit content. In the UK, law enforcement maintains and uses a standard national repository of IIoC, known as CAID (Child Abuse Image Database), to identify known illegal images by matching their image hashes and metadata. The CAID plays a significant role in making IIoC investigations faster and more effective. However, all images that are not matched through using CAID require manual analysis. Every image has to be viewed and verified as IIoC by investigators. The victim age estimation in the images (i.e., determining whether they are juvenile or adult as this would change the course of the investigation) is a crucial part of this verification process and takes time due to a large number of images to inspect, therefore impacting the speed of the investigation, and consequently victims. This is a time-consuming and challenging task for human investigators.