Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Digital Forensics shows how machine learning helps investigators cope with the scale and complexity of modern digital evidence. You learn to apply AI to core forensic tasks: triaging and analysing large volumes of files and logs, detecting anomalies and tampering, recognising media and deepfakes, and surfacing patterns across communications and devices. The course keeps forensic rigour central — chain of custody, admissibility and the need to explain AI-assisted findings. You finish able to reason about applying AI to a digital-forensics investigation responsibly. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in digital forensics — automating evidence analysis, detecting anomalies and tampering, and accelerating investigation across digital data.
📋 Course Objectives
1. Triage and analyse large volumes of digital evidence.
2. Detect anomalies, tampering and deepfakes.
3. Surface patterns across communications and devices.
4. Preserve chain of custody and admissibility.
5. Explain and defend AI-assisted findings.
👥 Who Should Enroll?
• Digital-forensics and investigation professionals
• Cybersecurity and incident-response teams
• Legal and law-enforcement technologists
• Students of forensic science
🚀 Key Learning Outcomes
• An understanding of AI in digital forensics.
• An investigation-acceleration perspective.
• A rigour-first forensic approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Evidence Handling and Admissibility
• Chain of custody, forensic imaging and write-blocking fundamentals
• Admissibility standards and the expert's duty to the court
• Where an automated finding must still be humanly verifiable to be usable
Machine Learning for Evidence Prioritisation
• Classification and clustering to triage terabyte-scale seizures
• Known-file filtering, hash sets and perceptual hashing for near-duplicates
• Precision and recall trade-offs when a missed artefact is a missed crime
Image, Video and Audio Forensics
• Source attribution: sensor pattern noise and compression fingerprints
• Manipulation and deepfake detection, and the fragility of current detectors
• Speaker and face recognition evidence: error rates and demographic disparity
Communications and Log Analysis
• Authorship attribution and its limits with short, informal text
• Anomaly detection over network and endpoint telemetry
• Timeline reconstruction across heterogeneous, clock-skewed sources
Reporting and Contesting AI Evidence
• Explaining a model-derived finding to a non-technical trier of fact
• Validation records, error rates and the questions opposing counsel will ask
• Bias, provenance of training data, and grounds on which AI evidence is challenged
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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