Forensic image analysis can be used to resolve difficult incidents, such as the increasing number of child sexual abuse cases. Although the United Nations and European Union countries are trying to fight these crimes, criminals are developing new methods to circumvent the measures taken. Sharing videos that do not contain any criminal elements and inserting child sexual abuse videos between frames is a new method that has been seen recently. This article proposes a new method to analyse the videos prepared this way. The proposed method is particularly advantageous for detecting and analysing videos that have been manipulated and embedded in different content and can be applied as a new technique.

When firearms are used, they leave unique marks on the fired bullets and shells. Examining these marks gives clues about the crime scene and the weapon of the crime. However, due to the increase in the number of incidents in which firearms are used, the increase in the amount of evidence makes ballistic investigations very difficult and prolongs the analysis time of the evidence. With the automatic image analysis and identification system, ballistic evidence can be examined very quickly and used to classify possible matching evidences. In this study, we present an approach based on Image Similarity Measurement for forensic examination of cartridges from automatic pistols. For this purpose, we used 500 images of 9 × 19 mm and 500 images of 7.65 × 17 mm cartridge cases obtained from 20 different brands and models of pistols. We divided the images of bullet cartridges into four different categories as breech face, firing pin, ejector mark, and combined evaluation, and we obtained these images from the BALİSTİKA 2010 system. We investigated the classification and matching performance of the obtained four category images using 4 different methods: Structural Similarity (SSIM), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Universal Image Quality Index (UQI). The results show that our Structural Similarity (SSIM) approach is effective in classifying and matching ballistic evidence.