SOLVSTRAT
AI-Driven Deepfake
Detection & Document Forensics
Rise of Synthetic Identity Fraud :
Standard KYC checks were failing to detect sophisticated fraud attacks, such as deepfake videos used in
liveness tests or digitally manipulated ID cards (synthetic IDs).
Processing Heavy Unstructured Data :
Analyzing video feeds (for liveness) and high-resolution images (for forensic OCR) required heavy
computational power that would slow down the main banking application if not isolated.
Latency vs. Accuracy:
The challenge was to run a computationally expensive proprietary AI model to score a video for deepfake
markers and return a "Fraud/No-Fraud" verdict within seconds to keep the user experience smooth.
We engineered a specialized AI microservice architecture on Azure designed to handle unstructured data and
high-compute workloads.
Hybrid Microservices (Node + Python) :
We used a "Best Tool for the Job" approach.
Node.js acts as the lightweight traffic controller, receiving the user's video/ID upload.
Python serves as the heavy-lifting worker, hosting the proprietary AI model and computer vision libraries
necessary for forensic analysis.
AI & Deepfake Pipeline :
Video feeds are streamed through the Python service which frame-by-frame analyzes the content for
artifacts (pixel inconsistencies, lack of blood flow monitoring/rPPG) to detect deepfakes.
ID Card images are processed to detect tampering (font anomalies, layer editing).
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