Blunder, Verena

English

Technical Disclosure Common

Technical Disclosure Commons Defensive Publications Series July 2020 DETECTION OF COUNTERFEIT COMPONENTS BY A KI Verena Blunder Bertrandt Ingenieurbüro GmbH Follow this and additional works at: https://www.tdcommons.org/dpubs_series Recommended Citation Blunder, Verena, "DETECTION OF COUNTERFEIT COMPONENTS BY A KI", Technical Disclosure Commons, (July 31, 2020) https://www.tdcommons.org/dpubs_series/3475 This work is licensed under a Creative Commons Attribution 4.0 License. This Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons. DETECTION OF COUNTERFEIT COMPONENTS BY A KI  Technical task: With subsequent invention, offers of counterfeit components or products on the Internet can be detected and removed using Internet monitoring and AI.  Initial situation: Currently, various approaches are used in internet monitoring for the recognition and detection of possibly counterfeit parts, components in the area of hardware and software with the aim of listing the possible counterfeits with a certain probability (prima facie evidence) in a pre-selection and then examining them for possible counterfeits by manual verification (or inspection by means of test purchases) in order to initiate countermeasures against the counterfeit products etc.  Solution: In this approach, modern additional links of an AI with trained algorithms are used as components, which enable an evaluation of possible counterfeit products. Thereby an AI is developed, which performs an exact verification (fake/non-fake) by comparison with existing (specially created) libraries and the additional linking by means of these trained algorithms (evaluations with the original, original behaviour, function, effect, properties or error codes). In doing so, the goal of an exact answer to the question of whether the product is a fake is sufficiently proven.   Currently, common internet monitoring methods can be used to manually detect possible counterfeit products based on the following parameters: - substantially lower prices for the advertised component - high ratings and ranking among the top 10/top 50 listed competitive offers - at first sight exactly the same or similar appearance to the original - at first glance, a reputable supplier (e.g. specialist in the field by using sellers' names that are consistent with the component, such as the same name as the product) - Use of similar visual sales presentations (pictures, photos) as the original - Description as original from the manufacturer - Use of several keywords for finding by rough search parameters - Deceptively similar illustrations, not recognisable as fake to the layman, since the buyer usually has no knowledge of how the product is exactly constructed, designed and shaped (kind of trust in the brand) - Use of copied, unauthorized word-picture marks in the offer in order to pretend seriousness to the layman  In the first step, a certain pre-clustering of offers on the Internet can be made. In addition, these can be verified by manual inspection and verification methods, which can be very time-consuming. As a result, one receives potential indications of possible counterfeit products, which have to be proven by test purchases, etc.  In the following, the development and application of the methodology of AI is presented using the example of the navigation update of an automobile manufacturer from different year cycles, different hardware parts of the respective models and model years:   1.  The product to be checked (hardware) is available in its original form and can be used for further adjustments/verifications, etc. In the concrete example, the original navigation updates (physical delivery of the software update on CD, DVD, SD cards) differ from the respective predecessor product. The current original products differ from their respective predecessor versions due to obvious or marginal changes to e.g. image details, image content, year, part numbers, markings, brand logo changes, technical codes (back), etc.              Figure 1: Example DVD label with marginal changes (highlighted in yellow)  Marginal adjustments to the previous version (part numbers, years, number code) 2Blunder: DETECTION OF COUNTERFEIT COMPONENTS BY A KIPublished by Technical Disclosure Commons, 2020                     Figure 2: Example DVD cover with marginal changes (highlighted in yellow)  2.  The respective individual distinguishing features (cf. number 1 Figures 1 and 2) are collected from the original product per update, extracted and collected in libraries, whereby the overall views of the original including all facets (optical, technical, functional, etc.) are also stored here.  In detail the optical parameters are extracted from the original product (for libraries): - Image contents - Image details - Image chrominance and -luminance - Font sizes, font styles, font colors - Individual features (additional images, image content, certificates, codes, numbers, etc.) - distances, angles, technical machine codes on DVDs - Behaviour with certain procedures, processes, functions - Geometric conditions, center hole, etc.  - Machine codes on the back of the data carrier, numbers, barcodes, etc.   Depending on the product, additional technical, functional or software-related regulations, etc. can now be added. These are also included in the libraries and are available for further comparisons (not relevant in this example with navigation data carrier).  3.  A first automated evaluation of the offer texts of the alleged original product on the advertised offer channels is now carried out using the common internet monitoring method (see previous list). The pictorial representations of the alleged original offer are compared with the known original product features and parameters (from the libraries, see list in section 2) in a simultaneously running parallel evaluation and both evaluations from the text and picture information are linked with each other. In doing so, an automated comparison or verification of the alleged original offered on various web shops etc. with the original images, characteristics etc. of the product is carried out. The detected deviations of the alleged original from the original parameters are combined in an overall model, evaluated by algorithms and a qualified statement regarding the original/counterfeit to the offered product is presented as result.     Marginal adjustments to the previous version (part numbers, years, number codes, etc.) 3Defensive Publications Series, Art. 3475 [2020]https://www.tdcommons.org/dpubs_series/3475  Figure 3: First comparison possibilities for the algorithm  This methodology can therefore be applied and refined because a counterfeit product has been physically or digitally produced by the counterfeiter. This means that the counterfeit product exists in reality and has been presented and advertised by photographing the counterfeit product as an optical purchase aid for the potential group of buyers as a visible offer through pictures or textual descriptions, so that it appears to be an original.  4. The quality of the result of the evaluation is strongly dependent on the quality of the input variables/information from the evaluated individual information of the original product, from which the results were extracted for the AI In the end, a network of information is built up, which becomes more powerful and meaningful the more precisely and filigree the individual strands can be integrated, evaluated and verified into the overall structure. If individual parameters, image contents, offer texts, used part numbers, year numbers etc. do not match the parameters from the original, these deviations are calculated and evaluated by the algorithms of the AI. Here it is of great advantage that the learned parameters of the original product are already incorporated into the learning curves and training cycles of the AI.   5. In order to continuously improve the AI, it is imperative here to incorporate, use and follow up all individual parameters/recognitions of demonstrably counterfeit products in order to strengthen and refine the algorithms so that even more accurate results can be delivered in the future. This means that the algorithms of the AI are also fed and trained with the parameters (cf. figure 4, item 5) of verifiably counterfeit products to the original.           FAKE DVD Label (Colour Yellow)                  FAKE DVD Cover                 FAKE DVD Label    FAKE (Picture section)                                     FAKE                                                        FAKE   Figure 4       4Blunder: DETECTION OF COUNTERFEIT COMPONENTS BY A KIPublished by Technical Disclosure Commons, 20206. This in turn requires extraction of the parameters from the verifiably counterfeit products of the original, which in this case leads to additional, sometimes manual, effort. This additional effort is evaluated as a one-time effort, which can be performed once and reused as often as desired. This means that the effort does not have to be performed "individually and anew" for each verification. In this example (navigation updates), the proven counterfeit products and the resulting individual parameters are also extracted and incorporated into the AI models/algorithms, i.e. the AI is also trained with all known features of the counterfeits (see Figure 4). If this method of permanent data feeding (as described above) is continued for the AI on a long-term basis (the AI is trained continuously), further counterfeits of this kind can be evaluated in an automated and digital comparison and immediately recognized as a counterfeit (FAKES), without the need for a time-consuming manual verification etc. In an overall view, the methodology is to be extended additionally to possible counterfeit characteristics (physical, optical, software and data), these are evaluated and linked to the algorithms developed so far as further components of the AI. This above mentioned methodology can be used permanently, consistently and sustainably on all Internet marketplaces or for offers on websites and automated by scripts. By means of these findings and processes, how the counterfeit products are manufactured, or produced and processed, suitable countermeasures, e.g. of technical, constructional, optical, data technical and organisational nature, can be derived and incorporated into the sustainable process of product improvement or future new developments. In the sum of all measures to be applied, a counterfeit of an original with recovery from the counterfeiters' knowledge is to be classified here as "no longer economically viable". The mere fact that the counterfeit products, viewed in terms of time before they are actually sold to potential customers, are already recognized at this early stage (by the above-mentioned methodology) as FAKES at the first offer to potential groups of buyers, and thus the counterfeiter's economic proceeds and benefits can be regarded as low to almost non-existent, makes it clear that it is no longer profitable to invest in or enter the counterfeiting of products.   7. All in all, the classic internet monitoring is combined with the powerful AI component and is thereby independently (without manual assistance) enabled to deliver exact results from the evaluation and calculation of original vs. counterfeit.  8. Since in an internet monitoring the product is presented to the potential buyer in a tasty and usually descriptive way (through pictures, texts, etc.), this method can be used for every product/type of product with a link to an AI.  9. For the sustainable automated overall process as a final measure, an additional link/connector to the fully automated TAKE-DOWN routine is now being established in order to take sustainable and consistent action against products exposed as counterfeits (using the above-mentioned methodology) in the sense of a more immediate initiation of deactivation or deletion of the counterfeit offers. Thus one has an automated overall process, which determines and evaluates by trained algorithms as final result the offers with counterfeit products automatically removed from the Internet, which is to be classified as unique at present.  10. Considering the necessity of test purchases for the counterfeit offers, it must be clarified whether these are still absolutely necessary for the protection and additional verification or whether they can be dispensed with. Since the counterfeit products must exist in real life according to the illustrations in the offer, presentation and software (otherwise it would not be possible to display the image/offer photo), the question arises whether these counterfeit products (cf. Section 5, Figure 4) are also delivered to the buyer in this way or whether they are inferior copies etc. In this example with Navigation Updates per data carrier, normal blank DVDs, data carriers without picture or word marks are also delivered, there is only an indication (by felt-tip pen, by insert, etc.) of which product is involved. For the assessment by the AI, therefore, the parameters from image data, descriptions, characteristics, etc. from the offers with counterfeit products are sufficient. Since this is an automated process/method, conventional test purchases can only be made on a random basis in order to secure the algorithms of the AI in terms of quality observation.  11. As an outlook into the future, an evaluation of legal capacities to take action against these trademark infringements etc. should also be considered here. By using the above mentioned methods and above all by consistently following up the AI with constant learning and training and refining the algorithms for the products, by following up automated "take down's" it is rather to be seen as relief for the legal effort - independent of the current chances of success - in the respective countries for an effective enforcement of claims and offers a consistent and sustainable support in the success against counterfeits and their creators.  Advantages: - Detection of the offer of counterfeit products on the Internet  - consumer protection  - Automatic detection of the counterfeit by AI  - Removal of counterfeit offers on the Internet  - Reduction of counterfeit offers   5Defensive Publications Series, Art. 3475 [2020]https://www.tdcommons.org/dpubs_series/3475

DETECTION OF COUNTERFEIT COMPONENTS BY A KI

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DETECTION OF COUNTERFEIT COMPONENTS BY A KI

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