Tracking the UK trade in fakes - Part 1: Counterfeit hotspots

The trade in counterfeit products is a complex picture, involving detailed interactions between the online and offline worlds. The offline ('real-world') movement of products is a key element of the overall landscape, encompassing the supply chain for items to be sold online, as well as for physical retailers.

Stobbs' anti-counterfeiting function assists business with addressing this offline activity, working with customs and law enforcement to intercept and disrupt the transit of infringing goods, and provide intelligence and evidence on their source.

In this case study, we draw supply-chain insights by utilising aggregated data from interceptions and seizures of counterfeit products for three key clients in distinct industry areas:

• Client A: Clothing / apparel
• Client B: Food
• Client C: Manufacturing (tools and accessories)

The wider dataset covers activity which is international in scope, but in this first analysis we focus on activity relating to the UK only; that is, where either the sender (consigner) or recipient (consignee) location or both are in the UK. In the initial study, we consider all instances of sender and recipient locations together as a single dataset (since, in principle, both may represent intermediate stages in the overall supply chain of the infringing goods).

In order to provide a low-granularity overview, each location (usually represented as a full physical address) is assigned to its host town / city (hereafter referred to as the 'town'). From this dataset, we produce a bubble chart 'heat map', where the size of the circle for each town is proportional to the number of times it appears in the overall dataset (i.e. the frequency of its association with the trade in counterfeit goods as a transit point)^[1]. This overview is shown in Figure 1.

Figure 1: Bubble chart 'heat map' showing the frequency with which each town has been the identified location of a sender or recipient of counterfeit goods^[2]

The top level trends in the UK locations of counterfeit hostspots, as shown by the data visualisation, are as follows:

• Much of the activity is concentrated in a geographical brand running from south-east to north-west, with primary centres in and around Manchester, London, and Glasgow.

• Secondary centres of activity are apparent in Northern Ireland, the West Midlands, Edinburgh and eastern Scotland, and north-east England.

• Other features highlighted on the heat map include the transit routes between Glasgow and Edinburgh, and key port locations (e.g. Hastings, Newport, Aberdeen, Bridlington, etc.).

Detailed views of some of the key areas of focus are shown in Figure 2.

Figure 2: Detailed views of the counterfeit activity 'heat map': (a) southern Scotland and Northern Ireland; (b) Manchester and surroundings; (c) London and surroundings

It is then possible to conduct deeper dives within a specific town / region by mapping the specific locations at a postcode level. For Manchester (the largest overall focus of activity) for example, also encompassing the overlapping areas of Salford, Stockport, Oldham, Rochdale and Irlam, the distribution of the individual locations identified as being associated with the trade in counterfeit goods is as shown in Figure 3.

Figure 3: Individual locations of interest within the Manchester region (postcodes appearing twice in the dataset shown using pink markers; three times using orange markers)

This type of analysis can help to inform decisions on locations where further on-the-ground investigations and scoping exercises may prove fruitful. In the case of the Manchester mapping exercise, for example, the analysis successfully highlights a cluster of activity around the Cheetham Hill area to the north of the city, long recognised as a real-world location involved in the sale of counterfeit items^[3].

In the next part of this analysis, we carry out a more granular analysis of the data, separating out origin locations from destination locations. This helps us gain insights into the routes by which counterfeits are arriving into the country in cases where they are imported from overseas. It also highlights the regions from which they originate (to provide guidance on where customs training initiatives should be focused), and into locations from which counterfeit sales are being distributed (which may form the focuses of further on-the-ground investigation work).

References

[1] Note that there may be some 'overlap' within the data - e.g. in some cases a location of Rochdale or Oldham may have been categorised as being within the broader 'Manchester' area; however, the overall clustering of the data is still apparent from the final visualisation

[2] Coastline data source: https://www.evl.uic.edu/pape/data/WDB/

[3] https://www.bbc.co.uk/news/uk-england-manchester-67292006

This article was first published on 27 March 2024 at:

https://www.iamstobbs.com/opinion/tracking-the-uk-trade-in-fakes-counterfeit-hotspots

Web2/Web3 crossover - Part 3: Investigations and clustering

In the previous articles in this series^[1,2], we have considered the emergence of new links between content in the classic Web2 ('standard' website and Internet) environment and the decentralised, blockchain-based world of Web3 (including blockchain domains, cryptocurrency and NFTs). In this follow-up, we consider how additional insights on links between content across these environments can be gained through investigation and clustering techniques.

One of the principal appeals of Web3 content is the opportunity for high levels of anonymity (part of the picture involved in a desire for lower restriction and regulation), and a lack of ties to 'real-world' contact details. However, this does not mean that no open-source intelligence (OSINT)-style techniques can be applied. Many of the characteristics associated with Web3 content - such as cryptocurrency wallet addresses - are unique and distinctive, and can be tied to other findings where the same details are used, and can imply (for example) that a particular entity may be associated with multiple infringements.

In this article, we present two anonymised 'mini' case studies illustrating how these types of techniques can be applied. Key to this process is the existence of publicly available databases ('ledgers') of information relating to Web3 content, which can be accessed through Web2 infrastructure. One such example is the etherscan.io website.

The case studies concern instances where bad actors were infringing particular marks (referred to in this article as Brand1 and Brand2), to promote cryptocurrencies. This is similar to the examples presented in Part 2 of this series. The first case made use of an associated infringing blockchain domain (Brand1deployer.eth) for which details were available on the etherscan.io public ledger.

The results of a search for the blockchain domain name bring up a number of unique strings ('hashes') relating to the domain and its registrant (Figure 1).

Figure 1: Results of a search for Brand1deployer.eth on etherscan.io

Clicking on the 'resolved address' string for the domain brings up an additional page, giving details of transactions made to and from the domain / user (Figure 2).

Figure 2: Extracts from the etherscan.io page giving details of transactions associated with Brand1deployer.eth

In this case, the fourth row from the bottom is the most significant; the content of the 'To' column reads 'Create: Brand1', indicating that this transaction is associated with creation of the 'Brand1'-infringing smart contract (a blockchain program set to run when certain conditions are met, and typically used to execute the terms of an agreement). Clicking on this link brings up another page pertaining to the particular smart contract specifically, giving information on users who have purchased the currency and the associated transactions.

Similarly, the ledger website includes pages giving details and connections associated with the domain registrant, etc., which can serve as a basis for building further links.

The second case involved the cryptocurrency 'Brand2 Coin' or '$Brand2', which was available to be viewed and purchased through reputable decentralised exchanges (DEXs) such as UniSwap, and was also involved in the sale of NFTs via the OpenSea marketplace. This case also utilised a blockchain domain name (Brand2deployer.eth), but was additionally promoted across Web2 content. In this instance, an associated Twitter account ('@Brand2-2Coin') was set up and used to promote the infringing currency, with the smart contract hash (the string beginning '0xf484…') also explicitly given in some postings (Figure 3).

Figure 3: A Twitter (X) post advertising the 'Brand2 Coin' infringing cryptocurrency

The Twitter profile also gave the address of an associated Web2 website (Brand2coin.xyz) and a Telegram link (t.me/Brand2coinportal).

Using a similar approach to that shown above, it is possible to search for the smart contract string on the etherscan.io public ledger to reveal associated details, such as information relating to the contract creator ('Brand2deployer.eth').

This brief overview highlights that, although it may be unusual to establish links from Web3 content to 'real world' contact details, application of OSINT-style techniques can still be used to draw connections and reveal additional related findings, and can help to provide information surrounding infringing use of cryptocurrencies.

References

[1] https://www.iamstobbs.com/opinion/the-crossover-two-recent-developments-in-web2/web3-interaction

[2] https://www.iamstobbs.com/opinion/web2/web3-crossover-brand-related-crypto-infringements

This article was first published on 19 March 2024 at:

https://www.iamstobbs.com/opinion/web2/web3-crossover-investigations-and-clustering

Web2/Web3 crossover - Part 2: Brand-related crypto-infringements

Following on from our initial article on the emergence of connections between the classic Web2 world and the decentralised Web3 world^[1], we take a deeper dive into this evolving landscape. Linkages (or 'crossover') between the Web2 and Web3 ecosystems can take a variety of forms, including instances where specific domain names resolve to content in both environments (as is planned for the offerings on .box and .shib), cases where Web2 URLs 'map' to Web3 content (as for the eth.link service)^[2], and promotion of Web3 services and offerings within the Web2 landscape - or a combination of all of these.

One increasingly common manifestation of this crossover is the promotion of (potentially fraudulent) cryptocurrencies (or related services such as exchanges) claiming to be associated with, or endorsed by, well-known and trusted brands, or making unauthorised use of branded imagery. In many cases, this type of infringement occurs in conjunction with the use of a branded domain registration, typically featuring the brand name in conjunction with a relevant keyword such as 'coin' or 'token'. Indeed, this type of infringement is becoming sufficiently popular that - of the large numbers of such registrations - many are being monetised through the inclusion of pay-per-click links or offers to sell the domain names (in some cases for prices of $1 million+), and many others are currently inactive suggesting that they have been registered speculatively, or with the intention of subsequent use or sale.

As of February 2024, there are over 154,000 registered gTLD domains with names ending with 'coin', 'token' or 'deployer' (an additional keyword sometimes used in these types of infringement). Of these, over 500 also include the name of any of the top twelve technology brands^[3] (including organisations such as Apple, Google, Microsoft, Facebook and Amazon). Within this dataset, there are numerous examples of domains resolving to live sites using brand references to promote cryptocurrency-related content (Figure 1).

Figure 1: Examples of live sites using references to any of the top technology brands to promote cryptocurrency-related content (second-level domain (SLD) names (the part of the domain name to the left of the dot): applecoin, googlecarboncoin, amazonrivercryptocoin)

Beyond this list of companies, numerous other high-profile brands are also targeted in similar ways (sometimes using 'mash-ups' of multiple brands) (Figure 2), as are prominent figures such as technology leaders (Figure 3). Overall the X (Twitter) brand (together with Elon Musk specifically and the associated Grok AI brand) appear to be particularly heavily targeted (as also noted in previous articles^[4,5]).

Figure 2: Examples of live sites using other brand references to promote cryptocurrency-related content (SLD names: elonxtoken, grokcoin, gptcoin, aol-coin, grokacolacoin)

Figure 3: Examples of live sites using references to prominent technology leaders to promote cryptocurrency-related content (SLD names: bezos-coin, elonmarkcoin)

It is worth noting that several brand protection technology providers already offer VIP monitoring within their suites of services, in recognition of the fact that fraud campaigns of these sorts often capitalise on an outspoken SEO or brand leader, rather than simply the brand name. Individuals such as Elon, Bezos and Zuckerberg are commonly targeted, in addition to familiar figures in the Web3 world, such as Michael Saylor, the founder of Microstrategy. This type of abuse is well established in the Web3 world, but may be becoming more common generally, perhaps in view of the increasing 'media-savvy' nature of these industry leaders and their interactions with social media.

It is also informative to consider similar trends of infringement activity across the blockchain domain landscape since - by the very nature of the Web2/Web3 crossover - we might expect these types of scams to be associated with analogous registrations in the Web3 environment. Based on data from Dune Analytics^[6], we consider one year's worth of .eth blockchain domain registrations through the major Ethereum Name Service (ENS) provider.

The dataset gives 7,266 .eth blockchain domains with names containing 'coin', 'token' or 'deployer' registered between 22-Feb-2023 and 22-Feb-2024. Of these, 37 also contain the names of any of the top twelve technology brands considered previously, and an additional 69 were found to contain the names of other brands or individuals (including Twitter (and the misspelling 'Tvvitter'), Grok, GPT, Yahoo, iPhone, Blackrock, and Elon, Musk, and Bezos) commonly targeted in the same way. As of the time of analysis, none resolved to any live content, although this type of infringement clearly continues to be of active interest to bad actors, and means the space is worthy of monitoring for developments.

A final note is that there is no clear long-term trend over the one-year analysis period, although there were two notable spikes in activity on 10 and 12-Dec-2023 (Figure 4). Many of the associated domains appear to constitute 'clusters' of similar names targeting prominent celebrities, with multiple examples of domains of the form [name]deployer.eth all registered at the same time, presumably by one or more related entities. For example, a batch of 45 celebrity infringements is recorded at 10:45 on 12-Dec-2023, with examples including jeffbezosdeployer.eth, andrewtatedeployer.eth, johnnydeppdeployer.eth, arnoldschwarzeneggerdeployer.eth, kaynewestdeployer.eth (possibly a non-deliberate typosquat?), angelinajoliedeployer.eth, scarlettjohanssondeployer.eth, edwardsnowdendeployer.eth, mileycyrusdeployer.eth and dollypartondeployer.eth.

Figure 4: Daily numbers of registrations of ENS .eth blockchain domains with names containing 'coin', 'token' or 'deployer'

References

[1] https://www.iamstobbs.com/opinion/the-crossover-two-recent-developments-in-web2/web3-interaction

[2] https://www.iamstobbs.com/opinion/trends-in-web3-part-1-a-look-at-blockchain-domains

[3] https://www.top10-websitehosting.co.uk/biggest-brands/

[4] https://www.iamstobbs.com/opinion/x-trademarks-the-spot-not-a-textbook-example-of-a-successful-rebranding-exercise

[5] https://www.iamstobbs.com/opinion/cant-stop-the-grok-domain-infringements-following-xs-ai-brand-launch

[6] https://dune.com/queries/7507/14878

This article was first published on 13 March 2024 at:

https://www.iamstobbs.com/opinion/web2/web3-crossover-brand-related-crypto-infringements

Finding the fakes: another application of keyword-based filtering

Introduction

In previous Stobbs studies, we have outlined how relevance keywords can be used for filtering down large sets of candidate webpages into the subsets most likely to be relevant to a particular issue of interest. In general, the approach involves looking on each webpage for instances of the keywords in close proximity to the name of the brand of interest, and calculating a score for the page, based on the numbers of such pairs and the proximity of the terms to each other in each case. The pages with the highest score are then the most likely to be of interest^[1]. This methodology is based on the technique used to calculate brand sentiment (which utilises sentiment keywords, but is otherwise essentially identical)^[2].

The ability to filter in this way is an essential component of any brand-monitoring solution, allowing the most relevant pages to be quickly and efficiently identified, and reducing the requirement for expensive analyst resource to manually review large volumes of results.

In this study, we apply the approach to the identification of e-commerce websites likely to be associated with the sale of counterfeit or otherwise infringing goods, from a pool of larger product-related sites, based on identification terminology typically used by infringing sellers. This is of key importance for any brand-protection programme aiming to tackle the most egregious infringements, where counterfeits often present the greatest risk to revenue, and are typically the most readily enforceable.

The methodology utilises a list of 'high-risk' e-commerce keywords, which have previously been identified as being highly diagnostic of non-legitimate products when used in conjunction with a brand name. Examples include 'dupe', 'mirror-quality', 'A-grade', and so on. These terms are most usually applied to fashion and luxury products, but can be applicable to other areas.

In this study, we focus on general Internet content (i.e. 'standalone' e-commerce websites identified through search-engine queries), rather than on marketplaces specifically. This is partly because issues with general Internet content are, in general, the hardest to solve (because of the greater difficulty in finding relevant content, and the limitless range of ways in which product information can be presented on the page), but also because some marketplaces explicitly prohibit the posting of listings with branded product references (meaning many sellers will resort to use of brand misspellings and variations). Accordingly, an approach able to handle the analysis of general content can usually be applied to more well-defined channels (though with appropriate modifications, where necessary).

Findings

Proof-of-concept 1: luxury brands

In the first proof-of-concept, we consider the online sale of luxury-branded products, using Gucci and Chanel as representative brand examples. In each case, we generate a 'pool' of candidate pages for analysis by collecting the results returned by search engines (the first page of Google.com results, in this case) in response to query-terms consisting of the brand name combined with product-related keywords ('handbag', 'shoes', etc.) and e-commerce-related (and especially 'high-risk') keywords (such as 'buy', 'shop', 'cheap', 'discount', 'replica', etc.). For each brand, this yielded just under 2,000 unique webpages for analysis. Of the pages which were accessible for analysis by our automated tool, the datasets produced 252 results yielding non-zero (positive) scores for Gucci, and 222 for Chanel. The assertion is that these pages are the ones most likely to be offering the sale of counterfeit products, based on the use of 'high-risk' keywords near to the brand names (and, moreover, that the pages with the higher scores will, in general, feature greater numbers of such references and/or brand/keyword pairs with closer proximity). These would therefore be the priority candidates for further analysis and potential enforcement.

Manual inspection of the results suggests that this is indeed the case, and almost all of the non-zero-scored pages feature what we would consider to be content of potential concern (Appendix A; Figures 1 and 2).

Figure 1: Examples of 'high-risk' pages for Gucci

Figure 2: Examples of 'high-risk' pages for Chanel

It is also worth noting that it may be necessary to 'tune' the keyword lists in response to the types of content being found and the terminology used. An earlier iteration using 'inspired' as a 'high-risk' keyword (e.g. in the context of 'Gucci-inspired' items) was found to lead to a less 'clean' categorisation of results, due the more generic nature of this term and its tendency also to be used in other contexts (e.g. "It was not just the high quality clothes of the wealthy guests that inspired Gucci" and "During the 1930s Gucci became inspired by horse racing"^[3]).

Proof-of-concept 2: electronics brands

The second case study considers electronics brands (using Samsung and Panasonic as representative examples, with appropriate product-related keywords - 'tv', 'smartphone', 'headphones', etc.), as an area where the sale of counterfeits is of particular concern because of the obvious safety implications.

In this case it was found that the keyword 'discount' (which was actually relatively diagnostic of 'high-risk' content for luxury brands) was less diagnostic for electronics, in part due to the large number of sites offering discount codes. This keyword was therefore removed from the list for this second analysis.

However, the approach was again effective, though a manual inspection of the positively-scored results found a smaller proportion of results of real potential concern than for the luxury brands, where the keywords are more directly applicable to infringing items. However, as in the first study, significant numbers of results of interest were successfully identified, and effectively prioritised using the keyword / scoring approach (Figures 3 and 4).

Figure 3: Examples of 'high-risk' pages for Samsung

Figure 4: Examples of 'high-risk' pages for Panasonic

Conclusion

Overall, the use of 'high-risk' e-commerce keywords in an analysis of proximity to brand terms has been found to be an effective way of prioritising sets of 'candidate' pages, in order to identify the subset which are most likely to be associated with the sale of counterfeits (even if the exact list may need to be 'tuned' to be most applicable to specific industry or product areas). The approach is therefore an important consideration in the deployment of automated brand-monitoring tools, to build efficiency into the analysis process and reduce the incidence of 'false positives'.

In the specific cases of the live infringements identified in this analysis, the fact that all (by definition) have been live for long enough to have been indexed by search engines, and are highly ranked in response to relevant query terms, should be a cause for concern for the respective brand owners. Assuming that these brands do have brand protection services in place, the analysis highlights that (depending on the methodologies used) in many cases, relevant results may be being missed, are not effectively being identified from within wider datasets of identified findings, or that enforcement attempts may have been ineffective (which may particularly be the case for some of the more problematic jurisdictions, such as with .ru and .cn domains) - or some combination of all of the above points. This conclusion highlights the requirement for an effective programme for detection, prioritisation, and takedown to be employed by brand owners.

Appendix A: The most highly scored pages for luxury brands

Table A.1: Webpage titles for all results assigned a potential relevance/risk score of 300 or greater for Gucci

Webpage title	Potential relevance score
Replica Gucci Handbags Wallets Gucci replica purses ...	4652
GUCCI REPLICA HANDBAG - SomaliNet Forums	4307
Gucci replicas expert - buy the best quality fake Gucci	3966
Gucci First Copy Shoes	3485
A 1:1 quality Gucci replicas online sale store	2829
Gucci Shoes Discount | ShopStyle UK	2515
Discount Gucci Belt	2400
Gucci Replica - RoyalPurse	2113
75% OFF Gucci Discount Code: (3 ACTIVE) Jan 2024	1912
Replica Gucci Womens Shoes Collection	1829
Best Replica Gucci Accessories on Topbiz.md	1688
Gucci Handbags Discount	1686
Replica Gucci Trainers - Casual Sneakers	1534
Sneakers for Men - Gucci Replica	1405
Moccasins and Loafers for Men - Gucci Replica	1405
Tech Accessories for Women - Gucci Replica	1405
Men Accessories && Wallets - Gucci Replica	1405
Fine Jewelry Archives - Gucci Replica	1405
Silver Necklaces for Men Archives - Gucci Replica	1405
Replica Gucci Ring	1396
Replica Gucci Sneakers For Men	1395
Elegant gucci replica For Stylish And Trendy Looks	1215
replica Gucci jewelry	1214
Bum Bag Gucci Dupe - Smart Accessories	1201
Fake Men's and Women's Gucci Shoes	1023
Gucci Replica Bamboo Bag Archives | Knock Off Designer ...	988
Gucci Discount Code - 10 Vouchers in January 2024	915
DesignerBagHUB cheap discount gucci replica jewelry ...	835
Buy replica gucci with free shipping on AliExpress	800
Finding The Perfect Replica Gucci Handbag	776
Gucci Shoes Archives	721
splurge and save: gucci princetown fur-lined leather slipper ...	692
Guides To Spot A Good Replica Gucci Bag	665
How Good Can Gucci Replicas Be?	664
GG Marmont Bags - Gucci Replica Handbags	656
Replica Gucci Shoes Wholesale Buying Guide 2020	649
Stunning Replica Gucci Jewelry at DesignerBagHUB	604
Vintage gucci replica (imitation) - Gem	598
Gucci GG Black small messenger Bag 523599	582
Gucci dupe shoes - thriftbiscuit	574
The Aldo Gucci Bag Dupe MUST HAVE -	572
Premium Gucci Formal Shoes On Full Cash On Delivery	539
Replica Gucci GG Marmont Bags | Purse-Area	518
Inspired Gucci Bags - The Same Look For Less - Pinterest	518
17+ Best Gucci Inspired Bags that Look Designer	515
Top Quality Gucci Replica Shoes	515
Golden Gucci Copy Ladies Bracelet Watch	504
THE GUCCI DUPE	500
Gucci Dupe Belt in Black	500
Gucci Replica Pendant for girls	403
3d ring replica Gucci 3D print model	403
gucci hats replica	400
Best Deals for Knock Off Gucci Bags	362
27 Gucci Replica Handbags ideas	361
High Quality Replica Gucci Handbags For Sale | Purse-Area	350
I thought I'd nabbed a bargain after buying Gucci bag dupe ...	350
Gucci Shoe Alternatives - Linn Style	337
first copy gucci bag	331
Gucci Dupe Purses - Rio Clemens	325
Gucci Bag Dupes - Linn Style	325
9 Gucci Loafer Dupes (Including the Ones I Bought!)	325
Handbags | Gucci Copy Hand Bag 🛍️ | Freeup	300
Gucci 1955 Horsebit Bags - Replica Designer Handbags	300
Slingbags | Gucci Replica Sling Bag - FreeUp	300
Mirror copy Gucci Sneaker shoes with freebies.	300
Replica Gucci handbag in Bristol	300

Table A.2: Webpage titles for all results assigned a potential relevance/risk score of 300 or greater for Chanel

Webpage title	Potential relevance score
Best 25+ Deals for Copy Chanel Bags	4092
Best 25+ Deals for Copies Of Chanel Bags	3631
Chanel Double Flap Medium Replica Bag Review (Caviar ...	2055
Top Chanel replicas - Affordable Luxury Inspired Handbags	2038
How to pick Chanel replica jewelry for love	1956
Replica Chanel Classic Flap Bag Full Review	1483
Chanel Replica Jewelry	1450
Best 25+ Deals for Copy Chanel Bags - Poshmark	1399
Replica Chanel Sandals	1375
Authentic vs. Replica Chanel Flap Bag: A Detailed Comparison	1177
Wholesale and retail best chanel Jewelry Replica	1173
Best Chanel Replica Bag – Full Review	1104
Chanel Replica Necklace	1100
Cotton High Quality Replica Chanel Beach Towel	1077
Stunning Chanel Necklace Dupes - Replica bags	993
replica chanel shoes	961
Chic Chanel Earrings Dupes - Budget-Friendly Luxury	944
Replica CHANEL	934
Chanel Handbags Replica	834
Replica CHANEL	834
Chanel Sneakers Real vs high quality Replica. - MyBizShare	717
Chanel Replica Handbag Reviews and Shopping	704
Chanel Promo Codes: 10% Off January 2024	604
Auckland fashionistas duped by replica Chanel handbags	600
12 Ways to Spot a Fake Chanel	600
Best Chanel replica store - Xpurse	593
Replica Chanel Shoes	507
Replica Chanel Bags, Cheap Chanel Bags 100% Satisfaction ...	474
Look For Less: 9+ Best Chanel Inspired Earrings	449
Blair Cap Toe Ballet Flat (Women) curated on LTK	430
Vintage, Chanel Replica, Huge Faux Pearl Necklace	409
Brand New Quality Replica Chanel Designer Womens ...	400
Vintage, Chanel Replica, Huge Faux Pearl Necklace	400
Best 25+ Deals for Chanel Lace Up Heels	375
REPLICA Chanel Sandals	362
8 Chanel Shoes ideas	336
Replica Chanel Chair	331
Replica Chanel Chair	331
Amazing Chanel Necklace with pearl replica, crystal and ...	318
Replica Chanel CC Logo Brooch	315
Dune's lockstockk sandals are back in stock for 2022	312
How to spot a Fake Chanel Bag	301
chanel replica handbag for sale	300
Designer Chanel Shoe Dupes - Stylish & Affordable	300
Chanel Replica Mirror	300

References

[1] https://www.iamstobbs.com/utilisation-of-relevance-keywords-ebook

[2] https://www.iamstobbs.com/online-brand-prominence-and-sentiment-ebook

[3] https://thevintagecompactshop.com/blogs/antique-and-collectible-history/gucci-a-brief-history

This article was first published on 5 March 2024 at:

https://www.iamstobbs.com/opinion/finding-the-fakes-another-application-of-keyword-based-filtering