Artificial Intelligence and Digital Forensics Survey

The purpose of this survey is to inform research on Artificial Intelligence for Digital Forensics, and Artificial Intelligence forensics. Digital forensic experts are invited to conduct this survey.

The estimated time to complete this survey is 6 minutes. Please complete all questions even if you do not have expertise in a specific area, as that is valuable data. Survey participants will not be identified in the dissertation. Personal and sensitive information is not requested.

This research will contribute to a master’s thesis on the Forensic Computer and Cybercrime Investigations course in University College Dublin. This research is being conducted by Michael Callan.

Please share this survey with other digital forensic experts, it will close on 28 July. If the survey is completed by over 100 participants, I will donate €200 to UNICEF to save and protect the lives of children.

3.How familiar are you with the concept of Artificial Intelligence (AI) in digital forensics (e.g. AI used in digital forensics for image analysis, video analysis, Deepfake detection, OSINT, etc.)?

4.Have you encountered any research or practical applications of AI in digital forensics during your work and/or research?

Yes

No

If yes, please list
(a)    the most common applications of AI in Digital Forensics you have encountered,
(b) whether it was research or practical application,
(c)   the AI domain (e.g. deep learning),
(d)    the type of AI (e.g. automated vehicles, self-organising maps, etc.), and
(e)   the purpose of usage.

Note: Below are common AI domains
1. Machine learning includes:
(a)    supervised learning (regression and classification),
(b)   unsupervised learning,
(c)    dimensionality,
(d)   Semi-supervised learning (self training, low density separation models and graph based algorithms),
(e)    reinforcement learning (dynamic programming, monte carlo methods, and heuristic methods), and
(f)     deep learning (Deep Feed Forward Networks; Convolution Neural Networks, Recurrent Neural Networks, Siamese Neural Networks, Transformers, Generative adversarial networks (e.g. Chat GPT, DeepFake), graph neural networks).
2. Planning (Planning and scheduling; searching; optimisation)
3. Integration and Interaction (multi-agent systems; robotics and automation; connected and automated vehicles)
4. Natural Language processing (machine translation, content extraction, question answering, information retrieval, sentiment analysis, text generation, topic modelling)
5. Expert systems (including decision support systems)
6. Speech (audio processing)
7. Vision (Machine vision, image recognition)
8. AI services
9. Reasoning (knowledge representation; automated reasoning; common sense reasoning)