Artificial Intelligence and Machine Learning Applications in Forensics Sciences
The future is moving forward, and Artificial Intelligence (AI) and Machine Learning (ML) is at the forefront. Industries and organizations all around the globe have been beginning to adopt AI and ML, bringing in transformations in workflows and processes.
But what about applications in forensics sciences? No doubt, there are numerous ethical and regulatory considerations in this field, as there should be as it affects the rest of the life of a person. Nevertheless, there have been projects popping up on innovative ways people have applied AI and ML to discover the truth and apprehend criminals.
Here are some interesting applications that I found.
Catching Pedophiles with Image Recognition
VICE released an video on using AI to identify people based on vein patterns on the back of their hands. From this, they could catch pedophiles who often record themselves performing heinous acts with their faces covered but hands visible. It was claimed that they could say with 100% certainty that, if the veins on the back of the hands do not match, the person was not the same. As of the making of the video, they have not found any two vein patterns, freckle patterns, or skin crease patterns that have matched. In a possibly landmark case, this evidence was heard in court in a case where a girl alleged her father sexually abused her. This has been used to help police secure over 40 life sentences in the UK.
This is still actively being researched by Lancaster University in a 5-year project called H-Unique. Their core data comes from citizens donating photos of their hands and filling out a short questionnaire.
Although there is not much detail on how they extract the vein patterns and compare images, I guess that they apply the same algorithms as those used in facial recognition. Potential algorithms that could have been used include Eigenface, Principal Component Analysis (PCA), or Convolutional Neural Network (CNN) approaches.
ML in Digital Forensics
As our lives become more intertwined with technology, cybersecurity becomes a important factor in ensuring our safety and privacy. Digital forensics serves to analyze the footprints that criminals leave when conducting malicious activity in the cyber world.
Besides common applications like filtering spam mail and recognizing suspicious activity in surveillance videos, AI and ML can also be for more complex use cases such as network and malware forensics. Some areas in network forensics include labeling malicious traffic, identifying DDoS attacks, and classifying attack behavior. For malware, AI and ML can assist in detecting and responding to malware. Also, with new AI tools at our disposal to generate falsified content such as deepfake images or videos, AI and ML can help detect manipulated photos and videos to prevent the spread of disinformation.
A research paper also highlighted the use of machine learning techniques in digital forensics for smart environments where various devices and appliances are connected to the internet. Since the amount of data collected by smart devices is immense, ML can be used to scan the source code of applications, analyze tracking logs, and detect suspicious files. This was put into practice to build an intelligent intrusion detection system to combat cyber attacks. By automating these processes, we can create a more secure ecosystem of smart devices.
ML in DNA Profiling
Now that the processing and analysis of complex, unstructured data is possible, many avenues for forensic DNA analysis are opened.
In a research paper, authors describe in detail how ML can be used to analyze biological data to find DNA and RNA markers. These markers can be utilized to confirm the origin and estimate the age of the sample. Other applications include predicting the ancestry which is more of a forensic intelligence application. ML can also add value to DNA evidence, such as allowing the possibility of estimating the interval post-mortem (after death) or reporting on its source and activity level.
Finally, with the advent of generative models, research is being undertaken on other use cases such as artificial aging of an individual’s photograph or age prediction from a facial image, which could be theoretically be used as evidence in court. Another exciting application is it may be possible in the future to predict physical traits such as eye color, hair color, skin color and even face morphology from a DAN sample. Taking it one step further, generative models could even simulate some images of potential perpetrators from a DNA sample alone.
What is in the future of AI and ML in forensics sciences? In my opinion, these are a few keys that lie in wait.
AI ethics will be critical for bringing projects to the real-world. It is well-known that models can be biased and cause unfair outcomes. One of the most famous examples is the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS). This model was essentially a tool for corrections to assess the risk of defendants sentenced to a crime. However, the model predictions were eventually found to discriminate against black defendants, wrongly labeling them as high risk at almost twice the rate of white defendants. Another case where AI failed has been in predictive policing. Since Black people are more likely to be arrested, predictive policing resulted in a disproportionate of police response to Black communities.
Regulations will need to be in place to effectively implement AI and ML in forensics science. As a starting point, balanced datasets that are not biased towards any one race should be necessary for any models that are used in forensics science. Model explainability is also crucial since findings will need to be communicated to a judge or jury. Many algorithms are still a black box so significant development will be required so a lay-person will be able to understand how a model makes its predictions.
Lastly, any decisions made for humans should not be automated or dependant on AI alone. Instead, AI and ML should be used to assist forensics professionals with their decision-making. As more cases arise with AI and ML evidence, courts will need to set precedents and make judgments on the validity and acceptance of the evidence. This would lead to certain techniques being admitted while others being throw out.
I am excited at what the future will bring and will certainly be on the lookout for cases where AI and ML are influencing forensics sciences. Hopefully, these examples have also made you more inspired to tackle projects in this field!
References
The Use of Machine Learning in Digital Forensics: Review Paper. https://www.atlantis-press.com/article/125984186.pdf
Machine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensics Investigations within Smart Environments. https://www.mdpi.com/2076-3417/13/18/10169#:~:text=Machine%20Learning%20(ML),and%20automate%20DF%20investigative%20procedures.
Machine learning applications in forensic DNA profiling: A critical review. https://www.sciencedirect.com/science/article/pii/S1872497323001692
