AI in Video Content Moderation Quiz

Answers and explanations appear as you take the quiz.

1. Why is video moderation described as 'multimodal'?
   • It must analyze images, motion, audio, and on-screen text together
   • It only uses one camera
   • It runs on multiple phones
   • It works only in one language
2. How can perceptual hashing help a platform identify a re-upload of known harmful video?
   • By proving the uploader intention from a single frame
   • By comparing the upload’s visual fingerprint with stored fingerprints of previously identified content
   • By detecting every new harmful video without a stored reference
   • By replacing all review and policy checks
3. Why do platforms still route many cases to human reviewers?
   • Humans are faster than computers
   • To save electricity
   • Context matters, such as distinguishing a news report from glorification of violence
   • Because AI cannot watch video at all
4. How does speech-to-text contribute to moderation?
   • It changes the video's color
   • It compresses the file
   • It blurs faces automatically
   • It transcribes audio so NLP can detect spoken hate speech or threats
5. What can a robust video fingerprint help recognize after some encoding or image changes?
   • A near-duplicate of previously fingerprinted content
   • The real-world identity of the person who uploaded the clip
   • The exact intent behind a speaker’s words
   • Any harmful idea that has never appeared in a reference set
6. Which emerging challenge is reshaping video moderation?
   • Videos getting shorter
   • Detecting AI-generated deepfakes and synthetic content
   • Fewer people uploading video
   • Slower internet speeds
7. Why can analyzing motion across consecutive frames improve video moderation?
   • It converts every frame into an audio sample
   • It proves who uploaded the video
   • It can help identify violent actions that are not visible in a single frame
   • It removes all context from the clip
8. What can OCR contribute to a video moderation pipeline?
   • Measuring the speaker’s heart rate
   • Restoring compressed pixels to their original source
   • Identifying the camera owner by serial number
   • Reading text overlaid in the video for review by text or policy classifiers