Discover how you can detect video tampering through Python-based digital forensic techniques. Together, we'll walk through a full workflow using a live Google Colab session (Jupyter Not.), working hands-on with real-world video evidence from the Tempi train crash case.

We’ll start with an intro in digital forensics and then we will be extracting frames from videos at consistent intervals, enhancing them with denoising and sharpening methods. You’ll see how to validate timestamp integrity through pixel consistency tests, edge detection, and histogram equalization. Next, we’ll identify suspicious blurring using Laplacian variance, pinpointing areas that may indicate deliberate manipulation.

You’ll learn how to detect clone stamping by comparing image blocks using cosine similarity, and we'll perform Error Level Analysis with Pillow to uncover compression anomalies. We’ll then go further, with advanced clone detection using Gabor filters and identifying unusual movements with optical flow analysis via the Farneback method. We’ll visualize suspicious alterations clearly by creating heatmaps to highlight problematic areas.

Finally, we'll compile all our insights into a clear, comprehensive PDF report, capturing our methodology, detailed analyses, and visual proofs directly showcasing findings from the high-profile Tempi Train Crash Case.

Join us for a practical, interactive session filled with techniques you can immediately apply to your own forensic and digital security projects.