H.O.T. AGMedia Forensics combines technical measurement, cross-signal analysis, media expertise and contextual review. H.O.T. AG examines digital ad delivery from request and rendering through visibility, interaction, device and browser consistency to traffic origin, publisher context and the supply chain. No single signal is treated as proof; what matters is the coherence of multiple independent evidence points.
Verification classifies defined quality attributes inside one measurement system. Forensics begins where those classifications need to be explained, challenged or compared with other evidence: was an impression only counted server-side or actually rendered? Does viewability match position and visibility state? Is there a plausible interaction path? Do device, browser, geo and supply signals agree?
Depending on implementation: ad request and response, slot dimensions, iframes, render state, visibility and exposure time, creative lifecycle, beacon sequence, click and touch paths, timing, browser and device consistency, GPU/rendering environment, geo and traffic origin, publisher and inventory context, and the consistency between those layers.
Legitimate technical edge cases can create isolated anomalies. A headless indicator, unusual user agent or short visit is not enough on its own. A finding becomes stronger when independent signals support the same hypothesis, for example contradictory device traits, no interaction, implausible timing and simultaneous supply or placement anomalies.
It checks whether different measurement layers describe the same reality. A server may report an impression while the client never completes rendering; viewability may be counted while the slot is offscreen or hidden; a click may appear without preceding pointer, touch or focus events. Such inconsistencies are often more informative than a single rate.
In-ad measurement examines the ad delivery itself: slot, render, viewability, timing, interaction and technical environment. On-site measurement examines what actually arrives at the destination: traffic source, device, browser, behavior and other plausibility signals. Combining both allows paid delivery to be compared with arriving traffic.
Because missing data are not automatically good data. Tags can be blocked, fail to execute or be bypassed; closed platforms expose only limited external signals. We therefore document what was actually measurable and do not treat N/A or missing client evidence as automatically fraud-free.
Technical anomalies are assessed in context. Domain and app identity, ads.txt and seller-chain context, ad density, MFA patterns, reseller routes, redirects, operator structures and OSINT can all be relevant. The question is not only where an ad appeared but how the inventory is monetized and who benefits economically.
We first define scope, decision question and measurement coverage. Then come data collection, signal analysis, classification of anomalies, cross-signal and contextual analysis, prioritization by relevance and concrete actions. Measurement, finding, interpretation and recommendation are documented separately.
Automated pattern recognition and AI can search large signal spaces and prioritize anomalies. The forensic conclusion remains contextual: technical faults, legitimate automation and intentional manipulation cannot be treated as equivalent solely because a model flags them.
No measurement sees everything. Walled gardens, server-side delivery, blocked tags, privacy limits and platform constraints can reduce observability. A serious audit makes coverage and limitations explicit rather than pretending every unmeasured impression or automated access can be classified with certainty.
Our working framework distinguishes Content / Platform manipulation from Identity manipulation and considers both layers according to whether a mechanism is directly advertising-relevant. Examples range from fake sites, ad stacking and tag manipulation to fake traffic, bots, device-ID rotation, residential proxies and click injection. Categories can overlap and the taxonomy is not exhaustive.
The framework separates Content / Platform manipulation from Identity manipulation and distinguishes directly ad-relevant mechanisms from mechanisms that are not directly advertising-specific.
Working model: categories can overlap. MFA is a supply/content/business-model pattern rather than one single fraud mechanism. The taxonomy is not exhaustive.
Explore taxonomy and methodologyH.O.T. AG charges no consulting fee for the analysis, forensic interpretation or findings session. Only FouAnalytics technical measurement costs apply and are disclosed before the audit.
See audit scope & conditions *Free refers to the H.O.T. AG consulting fee. Technical measurement costs remain chargeable.We define mandate, scope and responsibilities before the project begins – and clarify whether H.O.T. AG is needed as media consultant or independent audit function.