Tracking counterfeiting on the web with python and ml

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With the rise of online marketplaces, sellers can easily grow their business by offering their products to a larger number of people. However, this comes with the risk of endangering their reputation, brand trust and profits due to counterfeiting issues. This talk will present a highly scalable approach able to timely query marketplaces via Web scrapers, analyze and classify the obtained data using ML algorithms and summarize the results through visualizations. Join us during this Journey to learn how to identify counterfeit products and discover the underlying technical challenges.

Engineering

Tracking counterfeiting on the Web
with Python and ML
Valerio Cosentino
Software Engineer
PyConEs, October 3rd, 2021

queries
marketplace
product
URLs
How to write effective queries?
How to set the frequency of queries?
SEARCH

queries
queue
search
product
URLs
lambda queue
scraping
API calls
SEARCH

queue
extract
lambda Dynamo
product
URLs
products
info
EXTRACT
mandatory
ﬁelds
optional
ﬁelds

ANALYSIS
Dynamo Aurora
contents
transform

ANALYSIS
What is a relevant content?
What is a legal/illegal content?
Relevance Detection

ANALYSIS
What is a relevant content?
What is a legal/illegal content?
Relevance Detection
manual
text analysis
image features

ANALYSIS
What is a relevant content?
What is a legal/illegal content?
Relevance Detection
rule-based
manual
text analysis
feature analysis
manual
text analysis
image features

Takeaways
● Counterfeiting is a growing problem
● Python and Machine Learning can help
● Manual intervention is still needed
● The approach can be applied to other scenarios
What’s next?
● More data, more questions to answer
○ Evolutionary analysis
○ Comparative analysis

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Tracking counterfeiting on the web with python and ml

1. Tracking counterfeiting on the Web with Python and ML Valerio Cosentino Software Engineer PyConEs, October 3rd, 2021

4. [1] https://www.cbc.ca/news/business/marketplace-counterfeits-fakes-online-shopping-1.5470639 [2] https://apnews.com/press-release/pr-businesswire/ef15478fa38649b5ba29b434c8e87c94 [3] https://www.cnbc.com/2020/03/02/shop-safe-act-2020-cracks-down-on-counterfeits-on-ecommerce-platforms.html Buyer Marketplace Brand

5. Buyer Marketplace Brand [1] https://arstechnica.com/tech-policy/2021/05/amazon-seized-and-destroyed-2-million-counterfeit-products-in-2020/ [2] https://www.ebay.com/help/policies/prohibited-restricted-items/counterfeit-item-policy?id=4276#section1 [3] https://www.aliexpress.com/buyerprotection/how_to_be_eligible.html [4] https://ec.europa.eu/growth/industry/policy/intellectual-property/enforcement/memorandum-understanding-sale-counterfeit-goods-internet_en ? ? ?

6. How can a brand know if its products are being counterfeiting on the Web? search extract evaluate get crazy Can Python and ML help?

7. How can a brand know if its products are being counterfeiting on the Web? search extract evaluate get crazy Can Python and ML help?

8. EXTRACT ANALYSIS etc.. SEARCH REPORT How can a brand know if its products are being counterfeiting on the Web?

9. queries marketplace product URLs How to write effective queries? How to set the frequency of queries? SEARCH

10. queries queue search product URLs lambda queue scraping API calls SEARCH

11. queue extract lambda Dynamo product URLs products info EXTRACT mandatory ﬁelds optional ﬁelds

12. ANALYSIS Dynamo Aurora contents transform

13. ANALYSIS What is a relevant content? What is a legal/illegal content? Relevance Detection

14. ANALYSIS What is a relevant content? What is a legal/illegal content? Relevance Detection manual text analysis image features

15. ANALYSIS What is a relevant content? What is a legal/illegal content? Relevance Detection rule-based manual text analysis feature analysis manual text analysis image features

16. [1] https://www.amazon.com/report/infringement [2] https://sell.aliexpress.com/zh/__pc/77Y4QdcvjD.htm [3] https://pages.ebay.com/seller-center/listing-and-marketing/veriﬁed-rights-owner-program.html [4] https://merchant.wish.com/brand-protection/brand-violation-report Fake product URLs Takedown REPORT

17. Takeaways ● Counterfeiting is a growing problem ● Python and Machine Learning can help ● Manual intervention is still needed ● The approach can be applied to other scenarios What’s next? ● More data, more questions to answer ○ Evolutionary analysis ○ Comparative analysis

18. Q&A EXTRACT ANALYSIS SEARCH REPORT

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