Large platforms like Google do compute multiple signatures for most images they host or index, but “to what level” depends on product, policy, and risk. They don’t publicly disclose full details. Broadly, expect a mix of lightweight fingerprints for scale, plus deeper analysis for safety/abuse, search quality, and duplication.

What “scanning” typically means on big platforms

  • Basic ingestion processing (nearly universal):
  • Normalize orientation, generate thumbnails.
  • Compute cryptographic hash for storage dedup.
  • Compute one or more perceptual hashes or global embeddings for near-duplicate detection and search.
  • Extract basic metadata (EXIF/IPTC) when present.
  • Search and ranking signals (widely used for web/image search and hosting):
  • Visual embeddings for retrieval (ANN indexes).
  • Quality signals (resolution, compression artifacts).
  • SafeSearch-type classifiers (NSFW, violence, medical).
  • Landmark/object/category tags to improve retrieval.
  • Abuse and trust/safety (targeted but very common):
  • Known-harm fingerprint matching (e.g., PhotoDNA/CSA/Google’s own perceptual hashes) for child sexual abuse material (CSAM).
  • Malware/phishing indicators in images (e.g., QR codes leading to malicious sites).
  • Spam/near-duplicate clustering to reduce index bloat.
  • Content understanding (selective, product-specific):
  • OCR on text-heavy images/screenshots for searchability.
  • Logo/brand detection for shopping features.
  • Face/people detection for on-device organization in some products; regulated by privacy policies and regional laws. Public-facing face recognition use is tightly constrained.

Do they scan “all” images?

  • Web images indexed by Google:
  • When Googlebot discovers an image, it’s fetched and processed to some degree to support indexing, search, deduplication, and SafeSearch. Scale requires primarily automated, lightweight-to-medium processing, with more expensive analysis reserved for popular results or high-risk categories.
  • User-uploaded images to Google products (e.g., Photos, Drive, Blogger, YouTube thumbnails):
  • Generally processed on upload for storage, thumbnails, dedup, safety, and product features. The exact pipeline varies by product and user settings.
  • Google states some processing happens client-side or on-device for features like local face grouping in certain regions and versions; cloud processing is governed by product terms and privacy settings.

To what level? A practical mental model

  • Level 1: Always
  • Thumbnails, cryptographic hash, perceptual hash or embedding, basic metadata.
  • Level 2: Often
  • Visual embeddings for search, SafeSearch classification, near-duplicate clustering.
  • Level 3: When applicable
  • OCR for text-heavy images, logo/object tagging, quality scoring.
  • Level 4: Targeted/conditional
  • Geometric verification for dedup/cluster merges, deeper model inferences, or human review for policy enforcement.
  • Level 5: Specialized fingerprints
  • PhotoDNA/CSAM hashing, IP-rights fingerprint systems, robust watermark checks—applied wherever required by policy/law.

Privacy, policy, and regional constraints

  • Use of biometric identifiers (faces) and certain inferences is regulated (GDPR, CPRA, BIPA, etc.). Public search typically avoids personal identity recognition.
  • SafeSearch and abuse detection are justified under safety policies and legal obligations.
  • User controls: Some products allow toggles (e.g., face grouping in Photos), but baseline processing for storage/search/safety usually remains.

Why not just “scan everything deeply”?

  • Cost and latency at web scale: billions of images/day.
  • Diminishing returns: heavy models are reserved for re-ranks, top results, or flagged/risky content.
  • Legal and trust constraints: minimize sensitive processing unless needed and disclosed.

Takeaway for your project

  • At scale, expect:
  • A hybrid cascade: cheap signatures for everyone; heavier checks only for a small subset.
  • Safety/abuse fingerprints consistently applied.
  • Feature extraction for search relevance where it improves user experience.
  • If you need a similar approach, mirror this pattern: compute lightweight hashes/embeddings for all, reserve local-feature verification and advanced classifiers for shortlists or flagged items.

LEVELS

  • Yes: Most large platforms use a hybrid cascade—lightweight processing for all images, with heavier checks only when certain triggers fire.
  • The exact triggers and thresholds are proprietary and change over time.
  • Broadly similar patterns exist across big platforms (Google, Facebook/Meta, X/Twitter, TikTok, etc.), adapted to each platform’s product, risk profile, and policies.

Common trigger categories for “heavier checks”

While specifics are private, these categories are widely used:

1. Popularity and reach

  • High impressions/views, rapid re-shares, trending status.
  • Paid promotion/ads (stricter scrutiny).
  • Reason: More reach = higher potential harm/spam impact → allocate more compute.

2. Safety and policy risk

  • Hash matches against known-abuse databases (e.g., CSAM, extremist content fingerprints).
  • Model flags: NSFW/violence/gore, medical, self-harm, hate symbols.
  • User reports or automated anomaly signals (sudden spikes, coordinated posting).
  • Reason: Legal/policy obligations and trust/safety priorities.

3. IP and authenticity concerns

  • DMCA reports, brand/logo detections, watermark presence/absence mismatches.
  • Newsworthiness or public-figure context (misinformation risk).
  • Known-meme templates used for deceptive edits.
  • Reason: Rights management and misinformation mitigation.

4. Content-level uncertainty

  • Low confidence from early-stage models (borderline scores).
  • Disagreement between multiple signals (e.g., embedding similarity high but pHash far; or OCR contradicts tags).
  • Reason: Uncertainty budgeting—use heavy verification where it matters.

5. Structural/format cues

  • Detected frames/templates, large overlays, collages, or heavy edits.
  • Low-resolution or heavily compressed re-uploads of a known asset.
  • Reason: These obfuscations reduce early-stage reliability.

6. Account and network risk

  • New or low-reputation accounts, known spam clusters, bot-like behavior.
  • Cross-posts from previously flagged sources.
  • Reason: Risk-based routing of compute.

What “heavier checks” usually mean

  • Geometric verification (e.g., SIFT/ORB + RANSAC) on candidate matches.
  • Higher-capacity or ensemble classifiers (safety, manipulation detection).
  • OCR, logo/brand matching, robust watermark/fingerprint checks.
  • Cross-media correlation (compare to known originals, prior uploads).
  • Human review for the most sensitive cases.

Similar approach across platforms?

  • Conceptually yes:
  • Multi-stage pipelines.
  • Risk and reach-based triggers.
  • Known-harm fingerprinting as a universal baseline.
  • Differences:
  • Product goals (search vs. feed vs. messaging).
  • Legal regimes (EU vs. US vs. others).
  • Tolerance for false positives/negatives and the level of human escalation.

How to translate this into your own system

  • Always-on lightweight stage:
  • Compute cryptographic hash, pHash (or dHash), and one deep embedding.
  • Basic safety classifier if relevant to your use case.
  • Trigger logic examples:
  • Reach: If views > threshold or rapid reshares → expand top-K in ANN search and run geometric verification.
  • Risk: If any safety model score > soft threshold or hash matches a watchlist → run full verification + human review.
  • Uncertainty: If signals disagree (e.g., high embed similarity but poor pHash) → verify.
  • Structure: If border/overlay detected → run saliency crop + verification.
  • Account risk: New/flagged accounts → stricter thresholds and more verification.
  • Budget control:
  • Cap the percentage of items that can enter heavy checks per time window.
  • Use dynamic thresholds based on current load.