In recent days, following the media attention on the Kenosha Shooting trial (“STATE OF WISCONSIN – VS – Kyle H. Rittenhouse”) in which Amped FIVE has been used for evidence analysis, a previous article on this blog about the use of Artificial Intelligence on image and video forensics has been misunderstood and instrumentalized.
The applicability of image interpolation and image enhancement at large as evidence in court has been discussed, sometimes without the needed in-depth knowledge of the field.
In this article, we will clarify some very important concepts related to forensic video analysis at large and Amped FIVE.
1) Amped FIVE does NOT use Artificial Intelligence
Amped FIVE has been designed specifically for evidentiary use. It does not use Artificial Intelligence: image and video enhancement in Amped FIVE are implemented in a forensic workflow based on carefully selected algorithms that guarantee reliability, repeatability, and reproducibility. Thanks to this, Amped FIVE has become widely accepted as the standard tool for forensic image and video analysis, being used in 100 countries worldwide.
2) Interpolation DOES NOT TAMPER with the image
We need interpolation to show things as they are: interpolation is not only used to “zoom on” an image, but it is an essential part of the creation and display of a digital photo or video. Interpolation does not add image information, but improves visualization of image data 1,2,3. Questioning the general acceptability of interpolation means questioning the acceptability of images and videos as evidence.
3) Image enhancements performed by a competent analyst with the right tools are INSTRUMENTAL FOR COURT USE
An analyst with the right tools, technical preparation, and workflow can enhance the image in a way that can help the trier of fact and be accepted in court. Image enhancement is a fundamental part of forensic video analysis and it’s the duty of the forensic video analyst to properly enhance images and videos to give a more accurate representation of the scene, compensating, when possible, the imperfections introduced by the image generation process.
We hope this new take on the argument will help to better comprehend the topic and will clarify some of the misinterpretations of the original post. And if you want to learn more on these topics, please keep reading!