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Niv Sardi
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README: update TODO
Browse files- README.org +13 -5
README.org
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@@ -4,14 +4,19 @@ Detect spoofed website by detecting logos from bank and financial entities in
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pages with =ssl certificates= that do not match.
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The process is pretty simple:
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- [
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- [x] get logos, names and url
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- [x] navigate the url, extract the ssl certificate and look for =img= and tags
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with =id= or =class= logo (needs more heuristics) to make a db of logos
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- [x] screenshot the page and slice it into tiles generating YOLO annotations for
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the detected logos
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- [x] augment data using the logos database and the logoless tiles as background images
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- [
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- [ ] feed everything to a web extension that will detect the logos in any page
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and show a warning if the =SSL certificate= mismatches the collected one.
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@@ -20,13 +25,13 @@ The process is pretty simple:
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# build the training dataset
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docker-compose up --build --remove-orphans -d
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docker-compose exec python ./run
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-
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# run the training on your machine or collab
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# https://colab.research.google.com/drive/10R7uwVJJ1R1k6oTjbkkhxPDka7COK-WE
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git clone https://github.com/ultralytics/yolov5 # clone repo
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pip install -U -r yolov5/requirements.txt # install dependencies
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python3 yolov5/train.py --img 416 --batch 80 --epochs 100 --data ./ia/data.yaml --cfg ./ia/yolov5s.yaml --weights ''
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-
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#+end_src
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* research
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@@ -38,7 +43,7 @@ https://github.com/Hyuto/yolov5-tfjs
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there were a lot of augmentation solutions out there, because it had better
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piplines and multicore support we went with:
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- https://github.com/aleju/imgaug
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-
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but leaving the other here for refs
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- https://github.com/srp-31/Data-Augmentation-for-Object-Detection-YOLO-
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- https://github.com/mdbloice/Augmentor
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@@ -53,3 +58,6 @@ http://www.bcra.gob.ar/SistemasFinancierosYdePagos/Entidades_financieras.asp
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https://stackoverflow.com/questions/6566545/is-there-any-way-to-access-certificate-information-from-a-chrome-extension
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https://developer.mozilla.org/en-US/docs/Mozilla/Add-ons/WebExtensions/API/webRequest#accessing_security_information
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https://chromium-review.googlesource.com/c/chromium/src/+/644858
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pages with =ssl certificates= that do not match.
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The process is pretty simple:
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- [1/2] scrape gvt websites to get a list of entities.
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- [x] 🇦🇷 BCRA ok
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- [ ] other countries
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- [x] get logos, names and url
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- [x] navigate the url, extract the ssl certificate and look for =img= and tags
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with =id= or =class= logo (needs more heuristics) to make a db of logos
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- [x] screenshot the page and slice it into tiles generating YOLO annotations for
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the detected logos
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- [x] augment data using the logos database and the logoless tiles as background images
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- [2/3] train YOLO
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- [x] v5
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- [x] v6
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. [ ] v7 (actually slower than v6)
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- [ ] feed everything to a web extension that will detect the logos in any page
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and show a warning if the =SSL certificate= mismatches the collected one.
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# build the training dataset
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docker-compose up --build --remove-orphans -d
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docker-compose exec python ./run
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+
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# run the training on your machine or collab
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# https://colab.research.google.com/drive/10R7uwVJJ1R1k6oTjbkkhxPDka7COK-WE
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git clone https://github.com/ultralytics/yolov5 # clone repo
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pip install -U -r yolov5/requirements.txt # install dependencies
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python3 yolov5/train.py --img 416 --batch 80 --epochs 100 --data ./ia/data.yaml --cfg ./ia/yolov5s.yaml --weights ''
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+
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#+end_src
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* research
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there were a lot of augmentation solutions out there, because it had better
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piplines and multicore support we went with:
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- https://github.com/aleju/imgaug
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+
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but leaving the other here for refs
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- https://github.com/srp-31/Data-Augmentation-for-Object-Detection-YOLO-
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- https://github.com/mdbloice/Augmentor
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https://stackoverflow.com/questions/6566545/is-there-any-way-to-access-certificate-information-from-a-chrome-extension
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https://developer.mozilla.org/en-US/docs/Mozilla/Add-ons/WebExtensions/API/webRequest#accessing_security_information
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https://chromium-review.googlesource.com/c/chromium/src/+/644858
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+
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** papers
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https://logomotive.sidnlabs.nl/downloads/LogoMotive_paper.pdf
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