One impression, two verdicts
An impression fires. Vendor A returns it as billable. Vendor B returns it as invalid. The log line is identical; the verdicts are not. The instinct is to assume one vendor is broken, or one is lying. Neither conclusion is required by the evidence. The more useful reading is that GIVT and SIVT are not labels for what an impression is. They are labels for how hard it was to detect and what evidence was required to call it. Two vendors applying different lists, different models, and different corroboration thresholds to the same ambiguous event can legitimately land on different answers.
That distinction matters because it propagates. A viewable impression must first be a valid served impression. If two vendors disagree about validity, they are also disagreeing about whether the impression could be viewable at all, and therefore about whether it should be billed. The taxonomy is upstream of both measurement and money.
What the taxonomy actually says
The industry framework splits invalid traffic into two categories: general invalid traffic (GIVT) and sophisticated invalid traffic (SIVT). The Media Rating Council issued the final Invalid Traffic Detection and Filtration Guidelines, Version 1.0, and all MRC-accredited organizations must comply with them. The MRC addendum to those guidelines presents additional guidance for detection and filtration of invalid digital traffic, intended to apply to all accredited or certified measurers of digital advertising and content.
The critical structural point, as summarized in secondary industry write-ups of the MRC definition: the classification is based on how difficult the traffic is to detect, not on proving criminal intent. That is not a minor footnote. It means the taxonomy is a statement about method and evidentiary burden. It is not a ground-truth property that exists independently of the detector.
GIVT: routine, list-based filtration
GIVT is the easier category to manage because, in most cases, it is filtered automatically. The recognizable forms are list-based: known search engine crawlers and spiders, traffic from data centers, non-browser user-agent headers, and browser pre-fetch or pre-render requests. Ad platforms are expected to filter GIVT automatically using publicly available lists, which is why it rarely qualifies for a credit.
Read that expectation carefully. GIVT filtration is treated as a baseline obligation, not a value-added detection service. The economic consequence follows directly: if a platform is expected to have caught it, a vendor reporting it as billable is making a weaker claim than a vendor reporting SIVT with corroborating evidence. The label alone does not tell you which claim is stronger. The evidence class behind the label does.
SIVT: corroboration and human review
SIVT is built to look like a real user, which is why it causes the most financial damage and why it is harder to catch. Per the MRC definition cited in industry summaries, SIVT requires advanced analytics, multi-point corroboration, and in many cases direct human intervention to identify. It is usually intentional and can come from botnets running on hijacked devices or click farms staffed by real people mimicking genuine engagement.
The recognizable forms are broader than list entries: botnets and hijacked devices, ad injection and adware, click farms, domain spoofing, out-of-geo traffic manipulation, pixel stuffing and ad stacking, and misappropriated content. Each of these requires a different detection approach, and none of them resolves to a single lookup.
Why the same impression gets two answers
Four mechanisms produce disagreement without either vendor being wrong.
List freshness. GIVT depends on lists. A data-center IP range that appears on one vendor’s list but not another’s will be GIVT to the first and billable to the second. The impression did not change. The reference data did.
Model differences. SIVT depends on behavioral analytics and device fingerprinting. Two vendors weighting session duration, scroll depth, interaction timing, and device clustering differently will score the same session differently. A hijacked residential device generating plausible behavior may be SIVT to a vendor with device fingerprinting and billable to a vendor relying on list-based filtration.
Corroboration thresholds. SIVT requires multi-point corroboration. How many independent signals count as enough, and how strong each must be, is a methodological choice. A vendor requiring three corroborating signals and a vendor requiring two will disagree at the margin.
Borderline treatment. GIVT is usually unintentional and SIVT is more likely deliberate. Those qualifiers are doing real work. Traffic that is neither clearly infrastructure nor clearly fraudulent falls into a judgment zone, and different vendors draw the line in different places.
The hypothetical above illustrates the mechanism. It is not a case record, and it does not establish that any named vendor is more or less accurate than another. The retrieved sources do not support comparative accuracy claims, and none should be inferred.
How IVT classification reaches viewability and billing
The MRC viewable impression guidelines define a viewable ad impression as a served impression contained in the viewable space of the browser window, on an in-focus browser tab, based on pre-established criteria such as the percent of ad pixels within the viewable space and the length of time the ad is in view. But the guidelines also state that in all cases a viewable impression must meet pre-existing criteria for a served impression, including client-side counting and filtration for non-human activity. Those criteria are over and above the viewability criteria.
The same document is explicit about the dependency: each valid viewable impression originates from a valid served impression, and in no case should viewable impressions exceed served impressions counted on a campaign. It also defines invalid impressions as those that do not meet ad serving quality or completeness criteria, including non-human traffic or activity designed to produce fraudulent impressions, and notes that all fraudulent impressions are invalid but not all invalid impressions are fraudulent. Impressions known to be fraudulent can never be viewable impressions.
So the chain runs: IVT classification determines whether the impression is a valid served impression; valid served status is a precondition for viewability; viewability and validity together determine what gets billed and what can be credited. A disagreement at the IVT layer is not a cosmetic reporting difference. It changes the denominator for every metric downstream.
What the credit asymmetry means for operators
Meta and Google offer credit programs, not refunds, for documented invalid traffic, and the process usually requires evidence such as independent tracking data and billing discrepancy reports. GIVT often does not qualify because platforms are expected to filter it automatically.
That asymmetry is structural, not arbitrary. GIVT is defined as the category that routine filtration should have caught. Claiming credit for it is implicitly claiming the platform failed a baseline obligation. SIVT is defined as the category that requires corroboration, so a credit claim for SIVT is a claim backed by an evidence trail. The taxonomy’s evidentiary structure, not just its labels, determines who pays.
A reconciliation workflow
The following is a process recommendation, not a finding about any specific vendor’s accuracy.
- Log the label and the evidence class. Record which category the vendor assigned and what kind of evidence it cites: list match, behavioral model score, device fingerprint cluster, or human review. A label without its evidence class is not actionable.
- Compare against independent advertiser-side tracking. A pixel or tag on the advertiser’s own property provides a second observation point. Multi-point corroboration is the standard the SIVT definition itself invokes; apply it to the vendors, not just to the traffic.
- Treat discrepancies as questions about method. When two vendors disagree, the productive question is which list, model, or threshold produced each verdict. The unproductive question is which vendor is wrong, because the taxonomy does not supply a ground truth against which to answer it.
- Document the evidence trail for credit claims. If a credit claim is likely, the corroborating evidence needs to exist before the claim is filed. Independent tracking data and billing discrepancy records are the artifacts the process expects.
Where the documentation stops
Two limits should be stated plainly rather than papered over. First, the full text of the MRC IVT Detection and Filtration Guidelines was not available for direct quotation here; the GIVT and SIVT definitions above are drawn from industry summaries of the MRC framework and from the MRC addendum’s stated scope, not from the guideline body itself. Second, the retrieved OpenRTB documentation does not cover IVT-specific fields, so no claim is made here about how OpenRTB signals feed classification decisions. Where behavior is undocumented in the available sources, this article stops rather than speculating.
FAQ
Is SIVT the same thing as ad fraud?
Not exactly. All fraudulent impressions are invalid, but not all invalid impressions are fraudulent. GIVT is usually unintentional, and SIVT is more likely deliberate, but the classification is based on detection difficulty, not on proving intent.
Can an advertiser get a refund for invalid traffic?
The major platforms offer credit programs, not refunds, and the process usually requires documented evidence such as independent tracking data and billing discrepancy reports. GIVT often does not qualify because platforms are expected to filter it automatically.
Why can’t platforms filter all invalid traffic automatically?
GIVT is defined as the category routine, list-based filtration should catch. SIVT is defined as the category that requires advanced analytics, multi-point corroboration, and often human review. The second category is not automatable by definition, which is why it requires independent verification.
If two vendors disagree, which one should I trust?
Weight the verdicts by the evidence class behind them, not by the label. A SIVT finding backed by device fingerprinting and corroborating signals is a different kind of claim than a GIVT finding backed by a list match. The taxonomy tells you what kind of claim each vendor is making; it does not tell you which vendor is correct.
