Video Analysis AI

What is Video Analysis AI

“Analyze video” here means six concrete operations running in one pass: shot-boundary scene detection, content categorization by topic, key-moment extraction tied to attention curves, sentiment and topic analysis from the spoken track, object and face detection per frame, and OCR for any on-screen text. Upload a file or paste a YouTube, TikTok, or Vimeo URL. The report comes back with every detection bound to a clickable timestamp, so a 40-minute clip becomes a navigable index instead of a linear watch.

The pipeline runs computer vision on the visual track, automatic speech recognition on the audio, and an OCR pass on rendered text, then merges the three streams against a single timeline. 2,163,740 users feed it marketing footage, lecture recordings, product demos, surveillance clips, and competitor creative.

Benefits of AI Video Analyzer

• Process hours of video in minutes. Automated analysis runs about 100x faster than manual review.
• Scene, object, and emotion detection. Computer vision tags visual elements frame-by-frame at strong accuracy.
• Timestamped transcripts. Speech-to-text with sentiment analysis and clickable timestamps for every segment.
• On-screen text extraction. OCR reads slides, whiteboards, graphics, and overlays.
• Content quality flags. The AI surfaces pacing issues, attention drops, and weak structure.
• Exportable reports. Download PDFs, timestamped notes, or structured JSON.
• Free tier. One video analysis on signup, no credit card. Unlock unlimited via the 7-day Growth trial or Growth at $19/month annual.

How to Use Video Analysis AI

1. Upload a video file or paste a YouTube, TikTok, or Vimeo URL.
2. The AI analyzes every frame with computer vision for object detection, scene classification, and emotion recognition.
3. Speech-to-text transcription extracts audio with timestamped segments and sentiment scoring.
4. Visual OCR reads on-screen text from slides, whiteboards, graphics, and overlays.
5. Get a detailed report with scene breakdowns, engagement metrics, content quality scores, and recommendations.
6. Export or share as PDF, timestamped notes, or JSON.

The analyzer examines visual elements (objects, faces, text, logos), audio quality (clarity, background noise, speech patterns), content structure (pacing, transitions, key moments), and engagement signals (attention drops, high-value segments).

Your videos stay private. Processing runs on encrypted cloud infrastructure with GDPR compliance and SOC 2 Type 2 controls. Files are never used to train public AI models and are deleted after processing unless you save them.

Deepfake detection: what we actually check

Deepfake detection is a probability score, not a verdict. ScreenApp’s analyzer runs six independent signal checks and combines them into a confidence rating, with the underlying scores shown so you can see which signals fired. Below is the actual signal stack and what each one looks. ### The six signal checks

• Frame consistency — Examines how a face’s pixel-level identity holds across consecutive frames. Real faces deform smoothly; GAN-generated faces often have micro-jitter at the boundary between background and face (the “swimming” effect). Confidence: high for early diffusion models, lower for current state-of-the-art.
• Lip-sync alignment — Aligns audio phonemes with mouth shapes. Real speech has tight viseme-to-phoneme correspondence; many face-swap deepfakes break this within 200-400ms windows. False-positive risk: dubbed content (which has the same misalignment by design).
• Spectral fingerprints — Looks for GAN-family fingerprints in the frequency domain. Each generator family (StyleGAN3, Stable Video Diffusion, Pika, Runway Gen-3, Sora) leaves characteristic patterns in the high-frequency spectrum. We match against a catalog of 14 known generators.
• Audio waveform discontinuities — Voice-cloned audio shows micro-discontinuities at concatenation points. We sample the waveform at 24 kHz and look for energy spikes inconsistent with natural breath/pause patterns.
• EXIF and container metadata — Reads file metadata for editing-software fingerprints (which AI-video tools leave in the container) and creation/modification timestamps that don’t line up with claimed recording dates.
• Watermark detection — Scans for known C2PA / SynthID / provider-specific watermarks. OpenAI, Google, Meta, and Adobe all watermark their AI-generated outputs; finding one is a positive signal of AI generation (not necessarily malicious — it could be legitimate AI content).

What the score looks like

{
 "verdict": "likely_ai_generated",
 "confidence": 0.84,
 "signals": {
 "frame_consistency": { "score": 0.78, "fired": true },
 "lip_sync_alignment": { "score": 0.41, "fired": false, "note": "audio appears dubbed; signal unreliable" },
 "spectral_fingerprints": { "score": 0.91, "fired": true, "match": "Sora-2" },
 "waveform_discontinuities": { "score": 0.62, "fired": true },
 "exif_metadata": { "score": 0.55, "fired": false },
 "watermark_detection": { "score": 0.97, "fired": true, "type": "C2PA-OpenAI" }
 },
 "analyzed_at": "2026-05-13T14:22:18Z"
}

False positive rates

Deepfake detection accuracy depends on the source. Our April 2026 internal benchmark on 800 clips:

Content type	True positive rate	False positive rate
Recent AI-generated (Sora 2, Veo 3, Runway Gen-3)	92%	4%
Older AI-generated (DALL-E video, early Pika)	96%	3%
Heavy post-production (color grade, speed ramp, VFX)	n/a	8% (false alarms)
Standard smartphone footage	n/a	1.5%
Compressed/re-encoded footage (multiple times)	n/a	6% (compression artifacts mimic GAN artifacts)

Known weak spots

• Very short clips under 3 seconds — not enough frames for consistency analysis
• Heavily watermarked or branded video (lower thirds, station IDs interfere)
• Mixed-source footage (real intro + AI segment + real outro) — the analyzer reports a confidence range per chapter, not a single verdict
• Audio-only deepfakes are scored separately — visual checks don’t apply

Use the verdict as one input, not the conclusion. Pair the analyzer’s output with provenance checks (where did this clip come from? who first uploaded it? does it match other footage from the same event?) before publishing any conclusion.

Video Analysis AI Comparison - ScreenApp vs Competitors

Feature	ScreenApp	Vidpilot	Google Video Intelligence	AWS Rekognition Video	Azure Video Indexer	Twelve Labs
Interface	UI + API	UI	API only	API only	UI + API	API only
Scene detection	Yes	Yes	Shot change	Segment detection	Yes	Yes
OCR on frames	Yes	Yes	Yes	Text in video	Yes	Yes
Action detection	Yes (gestures, motion)	Limited	Activity recognition	Limited	Yes	Yes (search by action)
Custom models	No (pre-trained)	No	AutoML Video	Custom Labels	Person model training	Custom embeddings
Pricing model	Flat monthly ($19)	Flat monthly	Per-minute ($0.10+)	Per-minute ($0.10+)	Per-minute ($0.15)	Per-hour API
Free tier		Trial only	1,000 min/month first year	60 min/month first year	Limited free	Trial credits
YouTube URL ingest	Yes	Yes	Manual upload	Manual upload	Manual upload	Manual upload
Output format	PDF, JSON, notes	PDF, JSON	JSON only	JSON only	JSON, VTT	JSON, embeddings

How ScreenApp compares for video analysis:

• vs Vidpilot: Similar UI-first workflow, but ScreenApp exposes JSON exports and reads YouTube/TikTok/Vimeo URLs directly. Vidpilot focuses on creator workflows; ScreenApp handles arbitrary footage.
• vs Google Video Intelligence API: Google bills per minute and returns raw JSON. ScreenApp wraps the same detection types (shot change, label detection, OCR, explicit content) in a flat-rate UI with no SDK setup.
• vs AWS Rekognition Video: Rekognition requires S3, IAM, and a developer to wire it up. ScreenApp is point-and-paste with the same per-frame label coverage and adds engagement metrics.
• vs Microsoft Azure Video Indexer: Azure has the closest UI parity, including a player with insight overlays. ScreenApp lets you try one analysis on signup with no card, then offers a 7-day Growth trial, and pricing is flat at $19/month annual instead of per-minute.
• vs Twelve Labs: Twelve Labs is built for semantic video search via embeddings, aimed at engineering teams. ScreenApp targets analysts who want a finished report, not a vector index.

Who Uses Video Analysis AI

Ad-ops teams measuring competitor creative pull TikTok and YouTube ads from rival brands, run them through the analyzer, and get per-frame tags for hooks, product placements, CTAs, and pacing. The output feeds into creative briefs and A/B testing roadmaps.

News and broadcast analysts tagging footage index field recordings and press conferences by speaker, on-screen graphics, location signals, and quoted phrases. Researchers jump straight to the seconds that contain a specific topic instead of scrubbing tape.

Brand-safety teams scanning UGC review user-submitted clips before they go live on community platforms. Object detection flags weapons, branded property, and unsafe content; OCR catches text overlays the moderation rules cover; deepfake checks flag manipulated frames.

E-learning teams measuring engagement points correlate attention drops with specific lecture segments, then identify which slides, examples, or instructor pauses caused the dip. Course teams refine the cut and re-test against the same metrics.

Security and compliance analysts scan long-running surveillance for specific objects or events and use deepfake detection to flag synthetic or altered video through frame consistency and audio artifact checks.

FAQ

What is video analysis AI?

Video analysis AI runs computer vision and machine learning on video files. It detects objects and scenes, transcribes speech with timestamps, identifies emotions, reads on-screen text through OCR, and tracks engagement patterns across both audio and video in a single report.

Is the AI video analyzer free?

Free signup includes one video analysis (no credit card), covering scene detection, transcription, and object recognition. For unlimited analysis, start the 7-day Growth trial or subscribe to Growth at $19/month annual. Growth and Business plans add deepfake detection, emotion tracking, and priority processing.

Can it analyze YouTube videos?

Yes. Paste a YouTube, TikTok, or Vimeo URL and the tool processes it directly. You get timestamped insights on engagement, scenes, visuals, and audio without downloading the file first.

What can the AI detect?

Objects, scenes, faces, emotions, text overlays, brand logos, gestures, and movement. It transcribes speech with sentiment scoring, reads on-screen content through OCR, marks scene changes, rates video quality, and flags AI-generated or manipulated content through frame consistency checks.

How does the video describer work?

The describer combines object recognition, scene classification, OCR, and speech-to-text into a single timestamped narration. Use the output for accessibility compliance, SEO metadata, or summary notes.

Is it safe to upload sensitive video?

Yes. Files process with end-to-end encryption under GDPR and SOC 2 Type 2 controls. Videos are deleted after processing unless you save them, and nothing you upload is used to train public AI models.

How does ScreenApp differ from cloud video APIs like Rekognition or Google Video Intelligence?

The detection categories overlap (shot change, label detection, OCR, activity recognition, explicit content), but ScreenApp gives you a UI, flat monthly pricing, and direct URL ingest from YouTube/TikTok/Vimeo. The cloud APIs bill per minute, return raw JSON, and need a developer to wire up S3 or GCS first.

What is the best free video analyzer AI?

ScreenApp gives one full visual analysis (scenes, objects, OCR, transcription) free on signup with no credit card, plus a 7-day Growth trial for unlimited analysis. Google Video Intelligence offers 1,000 minutes free in year one if you can work with the API. Azure Video Indexer’s free tier is limited but includes a UI. Pick based on whether you want a finished report or raw JSON.

How do I analyze a video with AI?

Upload the file or paste a public URL. The analyzer transcribes the audio, indexes scenes, reads on-screen text, and tags objects and emotions. Results come back as a timestamped report within a few minutes for typical file sizes.