RESUMO
Childhood sexual abuse (CSA) is a worldwide phenomenon that has negative long-term consequences for the victims and their families, and inflicts a considerable economic toll on society. One of the main difficulties in treating CSA is victims' reluctance to disclose their abuse, and the failure of professionals to detect it when there is no forensic evidence (Bottoms et al., 2014; McElvaney, 2013). Estimated disclosure rates for child sexual abuse based on retrospective adult reports range from 23 % to 45 % (e.g., Bottoms et al., 2014). This study reports the four stages in the development of a Convolutional Neural Network (CNN) system designed to detect abuse in self-figure drawings: (1) A preliminary study to build a Gender CNN; (2) Expert-level performance evaluation, (3) validation of the CSA CNN, (4) testing of the CSA CNN model. The findings indicate that the Gender CNN achieved 88 % detection accuracy and outperformed the CSA CNN by 19 percentage points. The CSA CNN achieved 72 % accuracy on the test set with 80 % precision and 79 % recall for the abuse class prediction. However, human experts outperformed the CSA CNN by 16 percentage points, probably due to the complexity of the task. These preliminary results suggest that CNN, when further developed, can contribute to the detection of child sexual abuse.
