
AI AdBlocker wót eye3
AI-powered blocker (your path) Hybrid rules + on-page heuristic for Firefox MV2. Uses a machine learning model (running in the browser with ONNX Runtime) to see or analyze the DOM.
Někotare funkcije mógli se płaśenje pominaśNěkotare funkcije mógli se płaśenje pominaś
Za Firefox za Android™ k dispozicijiZa Firefox za Android™ k dispoziciji
1 wužywaŕ1 wužywaŕ
Trjebaśo Firefox, aby toś to rozšyrjenje wužywał
Metadaty rozšyrjenja
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Wó toś tom rozšyrjenju
This add-on is a starter AI Ad Block extension.
• The uploaded .zip is the Firefox-compatible build (no rules/ directory, uses background.scripts instead of service_worker).
• The uploaded source .zip contains the full, human-readable source code (TypeScript, scripts, manifests, build files) without dist/ or node_modules/.
• No remote code execution, dynamic code generation, or obfuscated code is used.
• Can detect hidden ads, sponsored labels, promoted posts, native ads that rule-based systems miss.
• Can adapt better if you re-train the model with new data.
Example AI Features You Can Add DOM / Heuristic Classifier:
Train a lightweight ML model on HTML snippets (features: tag type, attributes, text like “Sponsored”).
Content script grabs candidate nodes → runs model → hide if classified as ad.
Vision-based Ad Detection:
Use a small CNN (e.g., MobileNet/ONNX quantized) to check if an <img> looks like a banner ad.
Useful for “image-only” ads where markup doesn’t give them away.
Hybrid (most practical):
Use heuristics to filter likely candidates (divs with fixed size, suspicious classes, “sponsored” text).
Use ML to confirm → avoid false positives.
Future-proofing: when advertisers obfuscate HTML/CSS, rules break → but your ML model still generalizes.
Privacy-preserving: everything runs locally in the browser; no need to send page data to servers.
Research value: positions your extension as “next-gen” ad blocker, different from commodity ones.
• The uploaded .zip is the Firefox-compatible build (no rules/ directory, uses background.scripts instead of service_worker).
• The uploaded source .zip contains the full, human-readable source code (TypeScript, scripts, manifests, build files) without dist/ or node_modules/.
• No remote code execution, dynamic code generation, or obfuscated code is used.
• Can detect hidden ads, sponsored labels, promoted posts, native ads that rule-based systems miss.
• Can adapt better if you re-train the model with new data.
Example AI Features You Can Add DOM / Heuristic Classifier:
Train a lightweight ML model on HTML snippets (features: tag type, attributes, text like “Sponsored”).
Content script grabs candidate nodes → runs model → hide if classified as ad.
Vision-based Ad Detection:
Use a small CNN (e.g., MobileNet/ONNX quantized) to check if an <img> looks like a banner ad.
Useful for “image-only” ads where markup doesn’t give them away.
Hybrid (most practical):
Use heuristics to filter likely candidates (divs with fixed size, suspicious classes, “sponsored” text).
Use ML to confirm → avoid false positives.
Future-proofing: when advertisers obfuscate HTML/CSS, rules break → but your ML model still generalizes.
Privacy-preserving: everything runs locally in the browser; no need to send page data to servers.
Research value: positions your extension as “next-gen” ad blocker, different from commodity ones.
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Pšawa a datyDalšne informacije
Trjebne pšawa:
- Pśistup k wašym datam za wšykne websedła měś
Dalšne informacije
- Dodankowe wótzkaze
- Wersija
- 0.1.0
- Wjelikosć
- 2,89 MB
- Slědny raz zaktualizěrowany
- 9 dagar sidan (18. aug. 2025)
- Pśiswójźbne kategorije
- Licenca
- Licenca MIT
- Pšawidła priwatnosći
- Cytajśo pšawidła priwatnosći za toś ten dodank
- Wersijowa historija
- Wobznamjenja
- Zběrce pśidaś
Toś togo wuwijarja pódpěraś
Wuwijaŕ toś togo rozšyrjenja was pšosy, mały pśinošk pósćiś, aby jogo wuwiśe pódpěrał.
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It does not collect or transmit user data.
Reviewers can build the extension from source using the included scripts (see README).