Advertising formats
Blocking a request and hiding its space are separate outcomes. Choose rules that match both the unwanted behavior and your browser’s capabilities.
A banner can arrive through an image request, a script, an embedded frame, or content already included in a page. Network filtering targets a matching request. Cosmetic filtering changes which page elements are shown by a compatible browser tool.
These controls can complement each other, but a hidden element does not prove that every associated request was prevented. An empty space can also remain after a request is blocked. Inspect the outcome you actually care about.
A domain-level decision may affect more than a single advertising placement if useful resources share the hostname. A broad page selector may remove legitimate navigation or editorial content that uses a similar layout.
Prefer a specific explanation of what the rule targets. When reviewing a mistaken match, record the page, the affected task, and the rule that caused the change. Avoid adding a broad exception before you understand the dependency.
Check page loading, keyboard navigation, consent choices, and the transition from the banner’s location to surrounding content. Try an ordinary reload and a new session. A screenshot alone cannot explain whether a delivery request occurred.
The analytics testing guide explains how to reason about observations and their limits. The domain list guide helps identify when the chosen layer lacks enough context.
An advertising image made with AI can use the same delivery infrastructure as any other image. The creative process is not a dependable universal matching signal. Evaluate delivery, placement, and documented rule scope rather than promising a distinct “AI banner” detector.
Read banner ads and AI advertising for practical comparisons, or use the buyer’s checklist to assess a proposed dataset.
A network request may be blocked while the surrounding layout remains. A compatible cosmetic rule may address the element, but should be tested for unintended effects.
No. Visual appearance alone does not establish whether requests or measurements occurred.
From Ad Block List Lab
Evaluate banner advertising by its delivery, page behavior, and filtering rules, with a practical framework for readers and publishers that avoids unsupported claims about detecting AI-generated creative everywhere.
6 min readA banner-focused list should come with a clear definition of what it covers, how it is maintained, and where you are allowed to use it.
Explore this topicAI can be involved in creative production, targeting, or optimization. None of those labels automatically supplies a reliable browser filtering rule.
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