
AI AdBlocker (for Android) ์ ์์: eye3
AI AdBlocker for Firefox Android. Build using background scripts (no service worker) Blocks obvious ad containers + simple heuristics; no remote calls. Bundles ONNX Runtime Web locally(WASM only); no CDN, No data collection, no telemetry.
์ผ๋ถ ๊ธฐ๋ฅ์ ๊ฒฐ์ ๊ฐ ํ์ํ ์ ์์์ผ๋ถ ๊ธฐ๋ฅ์ ๊ฒฐ์ ๊ฐ ํ์ํ ์ ์์
์ฌ์ฉ์ ์์์ฌ์ฉ์ ์์
์ด ํ์ฅ ๊ธฐ๋ฅ์ ์ฌ์ฉํ๋ ค๋ฉด Firefox๊ฐ ํ์ํจ
ํ์ฅ ๋ฉํ ๋ฐ์ดํฐ
์คํฌ๋ฆฐ์ท



์ ๋ณด
No user data is collected, stored, or transmitted. No remote endpoints. All ML/WASM runs entirely locally.
This add-on is a starter AI Ad Block extension.
โข The uploaded .zip is the Firefox-compatible build (no rules/ directory, uses background.scripts instead of service_worker).
โข The uploaded source .zip contains the full, human-readable source code (TypeScript, scripts, manifests, build files) without dist/ or node_modules/.
โข No remote code execution, dynamic code generation, or obfuscated code is used.
โข Can detect hidden ads, sponsored labels, promoted posts, native ads that rule-based systems miss.
โข 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 โSponsoredโ).
Content script grabs candidate nodes โ runs model โ 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 โimage-onlyโ ads where markup doesnโt give them away.
Hybrid (most practical):
Use heuristics to filter likely candidates (divs with fixed size, suspicious classes, โsponsoredโ text).
Use ML to confirm โ avoid false positives.
Future-proofing: when advertisers obfuscate HTML/CSS, rules break โ 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 โnext-genโ ad blocker, different from commodity ones.
This add-on is a starter AI Ad Block extension.
โข The uploaded .zip is the Firefox-compatible build (no rules/ directory, uses background.scripts instead of service_worker).
โข The uploaded source .zip contains the full, human-readable source code (TypeScript, scripts, manifests, build files) without dist/ or node_modules/.
โข No remote code execution, dynamic code generation, or obfuscated code is used.
โข Can detect hidden ads, sponsored labels, promoted posts, native ads that rule-based systems miss.
โข 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 โSponsoredโ).
Content script grabs candidate nodes โ runs model โ 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 โimage-onlyโ ads where markup doesnโt give them away.
Hybrid (most practical):
Use heuristics to filter likely candidates (divs with fixed size, suspicious classes, โsponsoredโ text).
Use ML to confirm โ avoid false positives.
Future-proofing: when advertisers obfuscate HTML/CSS, rules break โ 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 โnext-genโ ad blocker, different from commodity ones.
0๋ช
์ ๋ฆฌ๋ทฐ์ด๊ฐ 0๋ก ํ๊ฐํจ
๊ถํ ๋ฐ ๋ฐ์ดํฐ๋ ์์๋ณด๊ธฐ
ํ์ํ ๊ถํ:
- ๋ชจ๋ ์น์ฌ์ดํธ์์ ์ฌ์ฉ์์ ๋ฐ์ดํฐ์ ์ ๊ทผ
์ถ๊ฐ ์ ๋ณด
- ๋ถ๊ฐ ๊ธฐ๋ฅ ๋งํฌ
- ๋ฒ์
- 0.1.6
- ํฌ๊ธฐ
- 107.82 KB
- ๋ง์ง๋ง ์ ๋ฐ์ดํธ
- 10์ผ ์ (2025๋ 8์ 19์ผ)
- ๊ด๋ จ ์นดํ ๊ณ ๋ฆฌ
- ๋ผ์ด์ ์ค
- MIT ๋ผ์ด์ ์ค
- ๊ฐ์ธ์ ๋ณด์ฒ๋ฆฌ๋ฐฉ์นจ
- ์ด ๋ถ๊ฐ ๊ธฐ๋ฅ์ ๋ํ ๊ฐ์ธ์ ๋ณด์ฒ๋ฆฌ๋ฐฉ์นจ ์ฝ๊ธฐ
- ๋ฒ์ ๋ชฉ๋ก
- ํ๊ทธ
- ๋ชจ์์ง์ ์ถ๊ฐ
์ด ๊ฐ๋ฐ์ ์ง์
์ด ํ์ฅ ๊ธฐ๋ฅ์ ๊ฐ๋ฐ์๊ฐ ์ฌ๋ฌ๋ถ์ด ์์ ๊ธฐ์ฌ๋ก ์ง์์ ์ธ ๊ฐ๋ฐ์ ์ง์ํด ์ค ๊ฒ์ ์์ฒญํฉ๋๋ค.
eye3 ๋์ ๋ค๋ฅธ ํ์ฅ ๊ธฐ๋ฅ
- ์์ง ํ์ ์ด ์์ต๋๋ค
- ์์ง ํ์ ์ด ์์ต๋๋ค
- ์์ง ํ์ ์ด ์์ต๋๋ค
- ์์ง ํ์ ์ด ์์ต๋๋ค
- ์์ง ํ์ ์ด ์์ต๋๋ค
- ์์ง ํ์ ์ด ์์ต๋๋ค
It does not collect or transmit user data.
Reviewers can build the extension from source using the included scripts (see README).