What is streaming fraud?

Streaming fraud is any deceptive activity targeting video, music, podcast, and entertainment subscription services to extract free access, resell paid logins, or drain subscriber revenue. Operators usually break it into separate layers, each needing a different tool: Account layer: one person opening many free-trial accounts, cycling introductory offers under fresh identities, stretching one paid login across many households, or reselling a subscriber's credentials; Payment layer: fraudulent card use, chargeback abuse, and friendly-fraud disputes after content has already been viewed; Network layer: large-scale credential stuffing and automated traffic against login endpoints; Content layer: DRM bypass, stream ripping, and unauthorized redistribution; Royalty layer (music): bot networks farming fake plays to pull royalties from a platform. ShieldLabs covers the account layer at signup and login: it surfaces patterns indicating one person behind many accounts, one paying account stretched across many devices and countries, and logins from unrecognized devices, using persistent visitor and user identification that holds across sessions regardless of email, IP, or cleared cookies.

How do I prevent streaming fraud?

You prevent streaming fraud by running a tool at each layer where it appears, since no single product covers all of them. A typical stack runs these layers in parallel: Payment: a chargeback-prevention or payment-fraud platform screens card transactions and friendly-fraud disputes; Content: DRM and forensic watermarking protect the playback stream and trace leaked content; Network: a bot-management tool screens credential stuffing and automated signup floods; Policy: your own rules (concurrent-stream limits, household device caps, free-trial throttles per identity, geo-licensing checks) keep obvious abuse in check; Account: an identification tool surfaces patterns indicating one person operating many accounts, one paying account used across many devices and countries, and logins from unrecognized devices. ShieldLabs sits at the account layer: it returns a risk score and patterns at signup and login, so your code can hold or step up before a free trial converts, a bonus week is granted, or a shared login is upgraded.

How does ShieldLabs prevent streaming fraud and abuse?

ShieldLabs runs through a single JavaScript snippet on your signup, login, or account page and returns a real-time risk profile your own rules act on. On every visit it surfaces: a 0-100 risk score with a signal breakdown; persistent visitor and user identification across sessions, cleared cookies, and rotated IP; anonymity signals: VPN, proxy, Tor, anti-detect browser, IP reputation, and more; patterns in analytics, indicating account sharing, multi-accounting, account farms, and account takeover. When many free-trial signups trace back to one device, browser, or identity, the multi-accounting pattern fires; when one paying account shows logins from many different devices and countries, the account-sharing pattern fires. ShieldLabs delivers the score and signals via API and Webhooks, so your code can flag, hold, or step up an account before a trial converts, a household upgrade is offered, or a high-risk login completes.

Does ShieldLabs detect account sharing?

ShieldLabs surfaces the device and visitor patterns behind shared accounts at signup and login, not the playback sessions themselves. It distinguishes two kinds of sharing: Household sharing: passwords passed to family or friends across separate devices; Credential resale: logins sold or rented to strangers on third-party markets. When one paying account is used by multiple unrelated visitors across different devices and locations, ShieldLabs links those logins through accurate identification and raises the risk score as the account-sharing pattern fires. Your code can then prompt an upgrade to a higher household tier, require a one-time verification on a new device, throttle parallel logins, or flag the account for review, all without touching the playback session. ShieldLabs identifies the visitor and user behind each login; your rules decide whether to upgrade, hold, or step up the account, complementing the concurrent-stream limits in your player and the household policies in your billing system.

Does ShieldLabs work for any streaming service?

Yes. The mechanism is the same wherever one person can profit by opening many accounts, stretching a paid login, or getting into an account that is not theirs. ShieldLabs surfaces patterns indicating multi-accounting, account farms, account-takeover logins, and shared paying accounts at signup and login; your rules act on the downstream abuse each category cares about: Subscription video (SVOD): free-trial cycling, household password sharing, and resold credentials; Ad-supported and free streaming (AVOD/FAST): fake account inflation that distorts ad inventory and viewer-count metrics; Music streaming: shared family-plan accounts and student-tier abuse; Podcast and audiobook subscriptions: duplicate free-trial signups and household sharing; Live sports streaming: one-account-many-viewer abuse during peak events and short-lived credential resale around marquee games; News and premium paywalls: paywall bypass through fresh accounts on every visit. In every case ShieldLabs identifies the visitor and user behind the account at the signup and login layer; your rules decide whether to allow, prompt an upgrade, hold, or step up the account.

Is ShieldLabs streaming fraud prevention software?

Yes. Streaming fraud prevention software is a tool a platform integrates to run streaming fraud detection automatically instead of reviewing accounts by hand, and ShieldLabs is one, focused on the account layer. Concretely, ShieldLabs: installs as one JavaScript snippet on your signup, login, and account pages; returns a risk score and anonymity signals through a REST API and Webhooks; shows patterns indicating fake accounts, multi-accounting, account sharing, and account takeover in analytics. ShieldLabs identifies the visitor and user and surfaces the signals; your own rules decide whether to allow, hold, or step up an account. For the account-abuse layer that underlies most streaming fraud, it is the software that covers it.

How do I integrate ShieldLabs into a streaming platform?

You integrate ShieldLabs by adding one JavaScript snippet to your signup, login, or account page; integration takes about 5 minutes. The steps: Install the snippet on the pages where viewers sign up, log in, or manage their account; Pass an optional UserHID (a hashed account or subscriber identifier from your own system) once a viewer signs up or logs in; Read the risk score and anonymity signals on the next visit via REST API and Webhooks for server-side flows; patterns appear in analytics. ShieldLabs returns the first risk score on the first visit, and the free tier includes 5,000 one-time identifications with no credit card required, so you can test on real traffic before enabling production rules.

Will ShieldLabs block users automatically?

No. ShieldLabs blocks nothing on its own; it returns a risk score and a full signal breakdown, and your code decides what to do at each threshold you set. For example: A mid risk score can prompt a household upgrade, flag a free-trial signup for review, or require one-time verification on a new device; A high risk score can hold a signup or a high-risk login for manual review; A low risk score lets real subscribers through with no extra steps. ShieldLabs keeps the decision in your hands, and the free tier includes 5,000 identifications so you can tune thresholds on real traffic before enabling rules in production.

Streaming Fraud Prevention

Stop media and streaming fraud.
End all subscription abuse

Prevent account sharing, fake free-trial accounts, and multi-accounting
across streaming, music, and entertainment subscriptions at signup and login

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Keep fake accounts off the free tier

Stop one person from cycling free trials and signing up under fresh identities on every welcome offer

Recover revenue lost to account sharing

Surface the account-sharing patterns that stretch one paid login across many households

Stop credential resale at the login

Prevent account-takeover logins from unrecognized devices and anonymized connections

Keep real subscribers moving

Legitimate subscribers sign up and log in without extra verification steps

How ShieldLabs prevents streaming fraud and abuse

ShieldLabs recognizes the real visitor behind every anonymous session

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, incognito mode, and rotated IP, even when they sign up under fresh emails

Anonymity Signals

Detection of VPN, proxy, Tor, anti-detect browser, IP reputation, and other anonymity signals on every visit, with up to 99% accuracy

Risk Score

A ready-to-use score reflecting the risk of each visit, with the weight of every signal behind it

Ready-Made Patterns

Detection of the hidden connections between accounts that may signal account sharing, multi-accounting, account farms, account takeover, and more

Real-Time Analytics

See how much of your traffic is masked, with an overall quality score and a breakdown of the sources sending your visits

Prevent streaming fraud and abuse in 5 minutes

Easily integrate into any signup, login, or account flow

1
Create your account
Sign up and get 5,000 free identifications.
2
Add the snippet
It identifies every visitor and returns their anonymity signals and a risk score
3
Check your traffic quality
See how much of your traffic is masked, with an overall traffic score
4
Use API & Webhooks
Apply the risk score and anonymity signals in your rules to stop fraud and abuse

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "public_ip": { "ip": "62.197.149.124", "country": "United States" },
  "local_ip": { "ip": "45.83.91.7", "country": "United States" },
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

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Stop media and streaming fraud.End all subscription abuse

How ShieldLabs helps