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CYBERSECURITY
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Classroom Contents
A Tour in the Real-World Challenges Faced by Law Enforcement Agencies When Dealing With Images
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- 1 Intro
- 2 ABOUT ME
- 3 WHAT WE DO
- 4 WHY THIS PRESENTATION?
- 5 WHAT IS FORENSIC VIDEO ANALYSIS?
- 6 WHY IS IT SO HELPFUL?
- 7 WHAT ARE THE ISSUES
- 8 VIDEO EVIDENCE SEEMS EASY, BUT IT'S ACTUALLY EASY TO GET IT WRONG
- 9 INFRARED: DON'T TRUST THE COLORS OF MOST CCTV FOOTAGE AT NIGHT
- 10 ANOTHER EXAMPLE
- 11 LENS DISTORTION: CAMERAS MAY CHANGE THE SHAPE OF THINGS
- 12 PERSPECTIVE: SIZE COMPARISON MAY BE TRICKY
- 13 COLOR FIDELITY IT'S HARD TO OBJECTIVELY EVALUATE COLORS ON VIDEO
- 14 WHAT COLORS DO YOU SEE?
- 15 COMPRESSION ARTIFACTS: HIDING OR ADDING DETAILS TO THE SCENE
- 16 VIDEO COMPRESSION
- 17 WHAT IS THIS...
- 18 PROPRIETARY VIDEO FORMATS
- 19 PROPRIETARY PLAYERS
- 20 SOMETHING WENT WRONG!
- 21 HOW NOT TO EXPORT
- 22 IMAGE QUALITY AND ENHANCEMENT/RESTORATION
- 23 PERSPECTIVE CORRECTION, DEBLURRING
- 24 DEINTERLACING, HISTOGRAM EQ, STABILIZATION, FRAME INTEGRATION, SHARPENING
- 25 EXPOSURE, STABILIZATION AND FRAME INTEGRATION
- 26 PERSPECTIVE STABILIZATION AND SUPERRESOLUTION
- 27 DEBLURRING A SCREEN PHOTO
- 28 I NEED THAT LICENSE PLATE... BORDERLINE!
- 29 WHAT CAN WE DO?
- 30 PROCESSING REQUIREMENTS FOR EVIDENTIARY USE
- 31 WHICH KIND OF ALGORITHMS ARE ACCEPTABLE?
- 32 THE IMAGE GENERATION MODEL
- 33 TRUST. DEEPFAKES, MANIPULATION, AND MISATTRIBUTION
- 34 FORENSIC VIDEO ANALYSIS: INTERPRETING IN THE RIGHT WAY WITHOUT BIAS
- 35 WE SEE WHAT WE WANT TO SEE
- 36 MEASURING HEIGHT
- 37 SPEED ESTIMATION
- 38 JUDGES AND JURIES ARE EASY TO MISLEAD ON TECHNICAL TOPICS
- 39 IMPACT OF ARTIFICIAL INTELLIGENCE
- 40 Al/ML/DEEP LEARNING ISSUES
- 41 GOING TO THE AVERAGE
- 42 BALANCE BETWEEN PRIVACY AND SECURITY
- 43 CYBERSECURITY
- 44 AMPED SOFTWARE
- 45 HOW TO CORRECTLY WORK ON IMAGE AND VIDEO EVIDENCE?
- 46 GUIDELINES
- 47 WHAT CAN WE OBTAIN?
- 48 CONCLUSIONS
