Bombay Stock Exchange, Jan 2026

📰 Medium · Machine Learning

Learn how deepfakes can be used to manipulate investors and the challenges of building effective deepfake detectors, with a focus on the gap between lab accuracy and real-world performance.

intermediate Published 30 Apr 2026

Action Steps

Build a deepfake detector using a dataset of videos and audio recordings
Test the detector in controlled lab conditions to achieve high accuracy
Deploy the detector in real-world conditions and evaluate its performance
Analyze the gap between lab accuracy and real-world performance
Improve the detector by incorporating more diverse and realistic data

Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding the limitations of deepfake detection tools and the importance of testing in real-world conditions. Product managers and entrepreneurs can also learn from the potential risks and consequences of deepfake technology.

Key Insight

💡 The accuracy of deepfake detection tools can drop significantly when deployed in real-world conditions, highlighting the need for more robust testing and evaluation.