The April 2019 edition of Significance, the journal of the Royal Statistical Society, features an article on the importance of objective measures and standards in evaluating evidence. Whilst many of the points made by the author are valid and applicable, practitioners within the digital / multimedia forensic sciences already have standards and guidelines for basing their work on an objective footing.
Investigators want to use video evidence to “identify” an object in a video – gun, knife, etc. But this “identification” is often complicated by a lack of nominal resolution in the region of interest. Many will simply state that the object has been identified, without actually taking the steps to formally conclude (prove) their assertions. They go with what they “know” (abductive reasoning), as opposed to what they can prove. There’s a better way.
First off, here are some relevant definitions from the SWGDE Digital & Multimedia Evidence Glossary. Version: 3.0 (June 23, 2016)
- Artifact: A visual/aural aberration in an image, video, or audio recording resulting from a technical or operational limitation. Examples include speckles in a scanned picture or “blocking” in images compressed using the JPEG standard.
- Image Analysis: The application of image science and domain expertise to examine and interpret the content of an image, the image itself, or both in legal matters.
- Image Comparison (aka Forensic Photographic Comparison): The process of comparing images of questioned objects or persons to known objects or persons or images thereof, and making an assessment of the correspondence between features in these images for rendering an opinion regarding identification or elimination.
- Image Content Analysis: The drawing of conclusions about an image. Targets for content analysis include, but are not limited to: the subjects/objects within an image; the conditions under which, or the process by which, the image was captured or created; the physical aspects of the scene (e.g., lighting or composition); and/or the provenance of the image.
- Nominal resolution: The numerical value of pixels per inch as opposed to the achievable resolution of the imaging device. In the case of flatbed scanners, it is based on the resolution setting in the software controlling the scanner. In the case of digital cameras, this refers to the number of pixels of the camera sensor divided by the corresponding vertical and horizontal dimension of the area photographed.
- Video Analysis: The scientific examination, comparison, and/or evaluation of video in legal matters.
It’s also important to define “forensic science.” For this, I’ll refer to “A Framework to Harmonize Forensic Science Practices and Digital/Multimedia Evidence.” OSAC Task Group on Digital/Multimedia Science. 2017: “Forensic science is the systematic and coherent study of traces to address questions of authentication, identification, classification, reconstruction, and evaluation for a legal context.”
What is a trace? “A trace is any modification, subsequently observable, resulting from an event.” You walk within the view of a CCTV system, you leave a trace of your presence within that system.”
SWGDE Best Practices for Photographic Comparison for All Disciplines. Version: 1.1 (July 18, 2017) provides a few more definitions.
- Class Characteristic: A feature of an object that is common to a group of objects.
- Individualizing Characteristic: A feature of an object that contributes to differentiating that object from others of its class.
With the definitions in mind, SWGDE Best Practices for Image Content Analysis. Version: 1.0 (February 21, 2017) provides the framework to begin the Content Triage step in the workflow – can I answer the question with the evidence file?
- 5. Evidence Preparation
5.2 Based on the request for examination, determine if submitted imagery is available to complete requested analysis. Determine whether submitted imagery is of sufficient quality to complete the requested examination, or if the image quality will have an effect on the degree to which an examination can be completed.
5.2.1 If the specified quality criteria are not met, determine if it is possible to obtain additional images. If the specified quality criteria are not met, and additional images cannot be obtained, this may preclude the examiner from conducting an examination, or the results of the examination may be limited.
(do I have sufficient nominal resolution within the target area to fulfill the request – answer the question? The nominal resolution, measured in pixels per inch or inches per pixel – or it’s metric equivalent, is the objective measure.)
- 6. Examinations Method
There is no one specific methodology for content analysis. The methodology for analysis will primarily be derived to answer the requested examination. However, any methodology applied to content analysis should incorporate an analysis of the imagery, the cataloguing of relevant features, an evaluation of the significance of the detected features, an evaluation of the limiting factors of the imagery, the formation of a conclusion, and a verification of the analysis. The repeatability (and / or reproducibility) of the procedure and documentation of the workflow is of paramount importance. Documentation should be performed contemporaneously.
6.1 Assess the contents of the image, to determine whether factors are present that can answer the examination request. The examination request generally will fall into one of the following categories:
6.1.1 Analysis to determine the conditions under which, or the process by which, the image was captured or created. Examples include, but are not limited to, the limitations of the recording device, and the inclusion of artifacts based on the file format or compression. This can help to answer the question “How does the recording system affect what is visible in the scene?”
6.1.2 Analysis to determine the physical aspects of the scene, including events captured. Examples include, but are not limited to, the lighting and composition of the scene, the presence of specific objects within the scene, a determination of the interaction between objects in the scene, and a description of events within a scene. This can help to answer the questions “Is a specific object visible in the scene?” or “What happened in the scene?”
6.1.3 Analysis to determine the classification of an object within an image. Examples include, but are not limited to, the make, model, and year of a vehicle, the determination of a manufacturing logo, and the determination of the brand and model of a weapon. This can help to answer the question “What is the object visible in the scene?”
6.1.4 Analysis to determine the location or setting of the image content. Examples may include either a general setting (e.g. Portland, Oregon) or a specific setting (e.g. Conference Room 23, the Northwest Corner). This can help answer the question “Where is the scene?”
6.3 Assess the image for features that contribute to the ability to form a conclusion, and record observed features. Consider the weight or importance of identified features, in order to determine the focus of the examination. Examples of features may include logos, shapes, reflections, or specific items.
How does one move from “artifact” (6.1.1) to “object” (6.1.3)?
As regards “artifact:”
When nominal resolution is low, the analyst must be certain of the provenance of the features within the region of interest in the evidence item. Are the “items of interest” actual representations of things, or are they an “aberration” in the image, resulting from problems in compression and etc. In order to move from “a few dark blocks of pixels” is not an “artifact” to the “few dark blocks of pixels” is an “object” – the rules for classifying an object apply. Additionally, one must rule out the possibility that what one sees is not an artifact. This must be done in a systematic / coherent fashion in order to be a forensic science exercise.
As regards “object:”
In order to move from “a few dark blocks of pixels” that are not a result of an “aberration” to firearm, there needs to be Class Characteristics present in the view that would lead to the conclusion of “object.” In order to form a conclusion, those Class Characteristics should be identified by the analyst – overall shape: the presence of features of an object that contributes to differentiating that object from others of its class Is the nominal resolution in the region of interest sufficient to conclude as to class and individualizing characteristics? This is the objective measure.
The standards and guidelines are there. It is left to the investigator, the analyst, the attorney, and the Trier of Fact to follow the rules and accept the facts as they present themselves. What does the investigator do when told by the analyst that the nominal resolution does not support a conclusion? Does he / she shop around for another analyst? The investigator must not fall victim to cognitive perseverance (bias) when faced with this new information.
Additionally, these stakeholders must understand when they’re on sound scientific footing or when they’re just taking their best shot.
Types of Resoning
- Abductive reasoning: taking your best shot
- Deductive reasoning: conclusion guaranteed
- Inductive reasoning: conclusion merely likely
What most in the forensic sciences don’t quite acknowledge is that they operate in the world of abductive reasoning most of the time.
From a US Constitution standpoint, from the standpoint of Constitutional Policing, it is for the side offering an item as “proof” of some condition (e.g., object = firearm) to actually prove class (e.g. class = firearm) as well as the individualizing characteristics that tie it to the offense and the accused. It perverts the cause of justice to require the defense to prove that it isn’t.
This is the level of detail that we get into when we present training in the Forensic Photographic Comparison, as well as our other educational offerings. You can find out these offerings, and sign up for a course by clicking here. We also engage in casework. If you need help on a case, or want a second opinion, feel free to contact us today.