Patent Publication Number: US-2023132941-A1

Title: Autonomous aesthetics-aware detection of counterfeit products

Description:
BACKGROUND 
     Recent years have seen significant improvement in hardware and software platforms for buying and selling products by electronic means. For example, developers have created technologies to modify or improve e-commerce platforms to provide information about and sell products. To illustrate, beside presenting product details including price, specifications, offers, and other details, conventional e-commerce systems often present product images to assist buyers. For example, conventional e-commerce systems often upload product images taken and submitted by sellers. Unfortunately, while e-commerce systems have become more popular and allow for expanded reach and greater ease of selling a product, e-commerce systems also have expanded the market for counterfeit products. Indeed, counterfeit products are commonly sold on-line as genuine products. Despite product images, detecting counterfeit products is not a straightforward or easy task. 
     SUMMARY 
     One or more embodiments provide benefits and/or solve one or more problems in the art with systems, methods, and non-transitory computer readable storage media that accurately and efficiently identify counterfeit products in digital images based on various features within the digital images. Generally, the disclosed system store key values computed based on the shape and other properties of authentic logos. To illustrate, the disclosed system extract a logo image including an identified logo from a digital image. The disclosed system generate an edge map of the authentic logo in the logo image to define a shape representation of an authentic logo. The disclosed system further register authentic color shades and gradients at each section of the authentic logo. Furthermore, the disclosed system register valid text styles and other properties for text used in the logo image. The disclosed system similarly extract properties from a logo within an uploaded image. The disclosed system then determine whether the uploaded image portrays an authentic or counterfeit product by comparing the features extracted from the authentic logo with features extracted from the logo within the uploaded image. 
     Additional features and advantages of one or more embodiments of the present disclosure will be set forth in the description which follows, and in. part will be obvious from the description, or may be learned by the practice of such example embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings which are summarized below. 
         FIG.  1    illustrates an environment in which a counterfeit identification system operates in accordance with one or more embodiments of the present disclosure. 
         FIG.  2    illustrates an overview diagram of determining that a digital image portrays a counterfeit object in accordance with one or more embodiments of the present disclosure. 
         FIG.  3    illustrates an example region proposal neural network used to extract a graphic element in accordance with one or more embodiments of the present disclosure. 
         FIG.  4    illustrates an overview of generating shape features for a graphic element in accordance with one or more embodiments of the present disclosure. 
         FIG.  5    illustrates an overview of generating color features for a graphic element in accordance with one or more embodiments of the present disclosure. 
         FIG.  6    illustrates an overview of generating text features for a graphic element in accordance with one or more embodiments of the present disclosure. 
         FIGS.  7 A- 7 B  illustrate an overview of comparing authentic graphic features with graphic features in accordance with one or more embodiments of the present disclosure. 
         FIGS.  8 A- 8 B  illustrate learning parameters for and applying a counterfeit detection model in accordance with one or more embodiments of the present disclosure. 
         FIG.  9    illustrates a schematic diagram of an example architecture of the counterfeit identification system in accordance with one or more embodiments of the present disclosure. 
         FIG.  10    illustrates a series of acts for determining that a digital image portrays a counterfeit product in accordance with one or more embodiments of the present disclosure. 
         FIG.  11    illustrates a block diagram of an example computing device in accordance with one or more embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     One or more embodiments of the present disclosure includes a counterfeit identification system that automatically determines whether a digital image portrays a counterfeit product by extracting and comparing authentic graphic features with graphic features from the digital image. In particular, the counterfeit identification system localizes an authentic logo image from an authentic digital image. The counterfeit identification system extracts authentic logo features from the authentic logo image. The authentic graphic features reflect properties of the authentic logo image including authentic shape features, authentic color features, and authentic text features. The counterfeit identification system further localizes and extracts a logo image from a digital image portraying a product. The counterfeit identification system further extracts graphic features for the graphic element including shape features, color features, and text features. The counterfeit identification system validates the graphic features by comparing the graphic features with the authentic graphic features. 
     To illustrate, in some embodiments, the counterfeit identification system extracts an authentic graphic element from an authentic digital image. The counterfeit identification system determines authentic graphic features for the authentic graphic element. For example, the counterfeit identification system determines authentic graphic features comprising one or more of authentic shape features, authentic color features, or authentic text features. In some embodiments, the counterfeit identification system extracts a graphic element from a digital image to be analyzed. The counterfeit identification system further generates graphic features for the graphic element. For example, similar to the authentic graphic features, the counterfeit identification system generates graphic features comprising one or more of shape features, color features, or text features. In some embodiments, the counterfeit identification system determines that the digital image portrays a counterfeit product based on comparing the graphic features and the authentic graphic features. 
     As just mentioned, in some embodiments, the counterfeit identification system extracts an authentic graphic element, such as a logo, from an authentic digital image portraying an authentic product. Similarly, the counterfeit identification system also extracts a graphic element from a digital image portraying a product to be analyzed. In some embodiments, the counterfeit identification system localizes and extracts the authentic graphic element and the graphic element by utilizing a Convolutional Neural Network trained using various graphic element digital images. In particular, the counterfeit identification system generates bounding boxes including the graphic element boundary, a mask region, and a confidence score indicating a likelihood that the bounding box includes the graphic element. 
     The counterfeit identification system further determines authentic graphic features for the authentic graphic element. Generally, authentic graphic features comprise key properties of authentic graphic elements. Authentic graphic features include authentic shape features, authentic color features, authentic text features, etc. In one or more implementations, the counterfeit identification system determines authentic shape features by computing an edge map of the authentic graphic element. In some embodiments, the counterfeit identification system generates the authentic color features by mapping pixels from the authentic graphic element into a color space to define an acceptable range of color values. Furthermore, the counterfeit identification system generate authentic text features by determining individual text properties such as position, style, warp, and color. 
     The counterfeit identification system also generates graphic features for the graphic element. Generally, the counterfeit identification system identifies properties of the graphic element that to compare against the authentic graphic element. In some embodiments, the counterfeit identification system generates graphic features that mirror the authentic graphic features. To illustrate, the counterfeit identification system generates shape features, color features, and text features for the graphic element. The counterfeit identification system, in one or more implementations, uses the same methods and procedures to generate the graphic features as used to generate the authentic graphic features. 
     The counterfeit identification system further analyzes the graphic features and the authentic graphic features to determine whether a digital image portrays a counterfeit product. In some embodiments, the counterfeit identification system compares the graphic features with the authentic graphic features to determine whether the digital image portrays a counterfeit product. In one example, based on determining that any one of the shape features, color features, or text features fall outside an acceptable threshold range of the authentic graphic features, the counterfeit identification system determines that the digital image portrays a counterfeit product. For example, based on determining that the shape of the graphic element is different than the shape of the authentic graphic element, the counterfeit identification system determines that the digital image portrays a counterfeit product. 
     In some embodiments, the counterfeit identification system intelligently analyzes the graphic features and the authentic graphic features utilizing a counterfeit detection model. In particular, the counterfeit identification system trains the counterfeit detection model using training images portraying both counterfeit and authentic products. The counterfeit identification system further trains the counterfeit detection model to generate one or more similarity confidence scores indicating a similarity between a graphic element and an authentic graphic element. The counterfeit identification system utilizes the counterfeit detection model to analyze graphic features of graphic elements and generate similarity confidence scores. Based on determining that at least one of the confidence scores corresponding to a digital image falls below a threshold similarity value, the counterfeit identification system determines that the digital image portrays a counterfeit product. 
     The counterfeit identification system also provides several technical benefits relative to conventional systems. Specifically, conventional e-commerce systems are often inaccurate and inefficient. In particular, a large proportion of products displayed and sold via conventional e-commerce systems are often uploaded by third-party vendors not associated with product manufacturers or the e-commerce system. Some conventional systems attempt to identify and remove counterfeit products; however, most conventional systems fail to do so accurately. To illustrate, some conventional systems utilize template or image matching techniques to label images as fake. Other conventional e-commerce systems analyze text descriptions of the product. However, many counterfeit products often mimic genuine products or corresponding text so closely that conventional systems often mislabel products as fake or genuine. 
     Due, in part, to shortcomings in accuracy, conventional systems often inefficiently utilize computational, storage, and communication resources. Conventional e-commerce systems expend computing resources to displaying and completing transactions for counterfeit products. Conventional systems often fail to identify products as fake until a purchaser has received and complained about a counterfeit product. Thus, conventional systems often expend additional communication resources receiving notifications of counterfeit products. Yet additional computing power must be utilized to remove counterfeit products. 
     Even conventional systems that attempt to identify counterfeit products before publishing them often function inefficiently. For instance, and as mentioned, some conventional systems rely on template or image matching to identify counterfeit products. Template matching often requires conventional systems to process large amounts of image data before identifying the target image or template. 
     The counterfeit identification system improves accuracy relative to conventional systems. The counterfeit identification system determines authentic graphic features that reflect various aesthetics and other visual properties of authentic graphic elements. In comparison with conventional systems that frequently rely on rigid and unrepresentative characteristics, the counterfeit identification system evaluates a wide range of properties of authentic graphic features including authentic shape features, authentic color features, and authentic text features. The counterfeit identification system further compares the authentic graphic features with corresponding graphic features for a graphic element from a digital image to evaluate whether the digital image portrays a counterfeit or genuine product. 
     The counterfeit identification system also improves efficiency relative to conventional e-commerce systems. The counterfeit identification system efficiently identifies counterfeit digital images portraying counterfeit products based on analyzing digital images. Accordingly, instead of expending computing resources to presenting digital images portraying counterfeit products and identifying counterfeits based on purchaser feedback, the counterfeit identification system automatically flags digital images containing counterfeit products before posting them. 
     Furthermore, the counterfeit identification system improves efficiency by extracting authentic graphic elements and selectively analyzing the authentic graphic elements. Instead of analyzing entire digital images, the counterfeit identification system, in one or more implementations, utilizes a machine learning model to intelligently identify portions of digital images and authentic digital images that contain an authentic graphic element or a graphic element. The counterfeit identification system further performs an analysis on the identified authentic graphic element or graphic element. Thus, the counterfeit identification system reduces the amount of computing resources required to generate authentic graphic features and graphic features. 
     As illustrated by the foregoing discussion, the present disclosure utilizes a variety of terms to describe features and advantages of the disclosed method. Additional detail is now provided regarding the meaning of such terms. For example, as used herein, the term “authentic graphic element” refers to a distinctive visual item. In particular, an authentic graphic element is a symbol or other design used by an organization to identify its products. For example, authentic graphic elements include a logo, symbol, design, or trademark specific to a given organization. An authentic graphic element is often affixed to a product portrayed within an authentic digital image. 
     Relatedly, as used herein, the term “graphic element” refers to a visual item affixed to a product within a digital image. In particular, a graphic elements include a logo, symbol, design, or trademark similar to an authentic graphic element. For example, a graphic element is often affixed to a product portrayed within a digital image. A counterfeit graphic element is intended to mimic an authentic graphic element. 
     As used herein, the term “authentic digital image” refers to an electronic representation of genuine subject matter, such as by a set of pixels. In particular, the term “authentic digital image” refers to a digital image portraying an authentic product from a given organization. An authentic digital image comprises any suitable format such as bitmap or a JPEG file format. An authentic digital image comprises a product image portraying a branded product originating from the brand&#39;s organization. More specifically, an authentic digital image portray an authentic product as identified by an authentic graphic element. 
     As used herein, the term “digital image” refers to an electronic representation of counterfeit or authentic subject matter. In particular, a digital image can include a digital image portraying a counterfeit product that is similar to an authentic product from the given organization. Alternatively, a digital image can portray an authentic product. For example, a digital image may be uploaded by a user (e.g., a third-party vendor) to an online content management system to represent an item being sold or considered for purchase by the user. Relatedly, the term “counterfeit digital image” refers to a digital image portraying a counterfeit product. 
     As used herein, the term “authentic graphic features” refers to characteristics or properties of an authentic graphic element. In particular, authentic graphic features include various distinctive features reflecting properties of an authentic graphic element. For example, authentic graphic features include key values computed based on the shape, color, text, or other component of an authentic graphic element. 
     Relatedly, as used herein, the term “graphic features” refers to characteristics or properties of a graphic element within a digital image. In particular, graphic features include various properties of the graphic element. For example, graphic features include values computed based on the shape, color, text, or other components of a graphic element. In one or more implementations, the graphic features may be similar to, but differ, from the authentic graphic features. 
     As used here, the term “authentic shape features” refers to properties relating to the shape of an authentic graphic element. In particular, authentic shape features include a shape representation that indicate the locations of edges of an authentic graphic element. For example, in some embodiments, authentic shape features comprise a shape vector/list including points indicating pixel locations of edges of a graphic element. Relatedly, the term “shape features” refer to a shape representation that indicate the locations of edges of a graphic element within a digital image. 
     As used herein, the term “authentic color features” refers to distinctive color properties of an authentic graphic element. In particular, authentic color features include color values and corresponding pixel locations within an authentic graphic element. The authentic color features comprise color values from a Hue Saturation Value (HSV), Hue Saturation Light (HSL), Red Blue Green (RBG), or other color space for each pixel location within an authentic graphic element. Relatedly, the term “color features” refers to color properties of a graphic element. For example, color features also include color values from an HSV, HSL, RBG, or other color space for each pixel location within a graphic element. 
     As used herein, the term “authentic text features” refers to distinctive text properties of an authentic graphic element. In particular, authentic text features reflect various text properties of text found within the authentic graphic element. For example, text features indicate the position of the text, text style, text warp, text color, and other properties of text within the authentic graphic element. Relatedly, the term “text features” refers to text properties of text found within a graphic element. Text features include one or more of the position of the text, text style, text warp, text color, or other properties of text within the graphic element. 
     As used herein, the term “counterfeit product” refers to a fake or unauthorized replica of an authentic product. In particular, a counterfeit product is a good made or sold under a given brand name without the brand owner&#39;s authorization. For example, a counterfeit product comprises a good having a graphic element intended to mimic an authentic graphic element. 
     The following disclosure provides additional detail regarding the counterfeit identification system in relation to illustrative figures portraying example embodiments and implementations of the counterfeit identification system. For example,  FIG.  1    illustrates a schematic diagram of a system environment (or “environment”)  100  in which a counterfeit identification system  106  operates in accordance with one or more embodiments. As illustrated, the environment  100  includes one or more server device(s)  102 , connected to a user client device  108  via a network  112 . While  FIG.  1    shows an embodiment of the counterfeit identification system  106 , alternative embodiments and configurations are possible. 
     As shown in  FIG.  1   , the server device(s)  102  and the user client device  108  are connected via the network  112 . As shown, in one or more implementations, each of the components of the environment  100  communicate via the network  112 . The network  112  comprises a suitable network over which computing devices are able to communicate. Example networks are discussed in additional detail below in relation to  FIG.  11   . 
     As shown, the environment  100  includes the server device(s)  102 . The server device(s)  102  generates, stores, receives, and/or transmits digital content including digital video, digital images, digital audio, metadata, etc. In particular, in one or more implementations, the server device(s)  102  provides digital content via web pages or native application to devices such as the user client device  108 . The server device(s)  102  is able to communicate with the user client device  108  via the network  112 . For example, the server device(s)  102  gathers and/or receives digital images including authentic digital images and/or digital images from the user client device  108 . The server device(s)  102  may also present digital images and authentic digital images at the user client device  108 . In some embodiments, the server device(s)  102  comprise a distributed server where the server device(s)  102  include a number of server devices distributed across the network  112  and located in different physical locations. The server device(s)  102  optionally comprises a content server, an application server, a communication server, a web-hosting server, or a digital content management server. 
     As further shown in  FIG.  1   , the server device(s)  102  includes an online content management system  104 . In one or more embodiments, the online content management system  104  comprises an e-commerce management system that facilitates the online purchase of products over the network  112 . The online content management system  104  also performs various backend functions associated with the online presence of a seller in order to facilitate the online purchase of products. In some embodiments, the online content management system  104  verifies the authenticity of products submitted to the online content management system  104 . For example, the online content management system  104  identifies digital images portraying counterfeit products. Furthermore, the online content management system  104  optionally performs other backend functions associated with the online presence of a seller. For example, the online content management system  104  generates web pages or other types of network content that are provided to the user client device  108  for the purpose of selecting items for purchase, rental, download, lease, or other form of consumption as will be described. 
     As illustrated in  FIG.  1   , the counterfeit identification system  106  is implemented as part of the online content management system  104 . Generally, the counterfeit identification system  106  intelligently identifies digital images portraying counterfeit products by comparing graphic features in the digital image with authentic graphic features. In particular, the counterfeit identification system  106  extracts an authentic graphic element from an authentic digital image and determines authentic graphic features, including authentic shape features, authentic color features, and authentic text features, from the authentic graphic element. The counterfeit identification system  106  further extracts a graphic element from a digital image and generate graphic features for the graphic element. The counterfeit identification system  106  further determines whether the digital image portrays a counterfeit product based on comparing the graphic features and the authentic graphic features. 
     As illustrated in  FIG.  1   , the environment  100  includes the user client device  108 . The user client device  108  is able to generate, store, receive, and send digital data. For example, the user client device  108  communicates with the server device(s)  102  via the network  112 . The user client device  108  illustrated in  FIG.  1    may comprise various types of client devices. For example, in some embodiments, the user client device  108  is a mobile device such as a laptop, tablet, mobile telephone, smartphone, etc. In other embodiments, the user client device  108  includes non-mobile devices, such as desktops or servers, or other types of client devices. Additional details regarding the computing devices, of which the user client device  108  is one implementation, are discussed below with respect to  FIG.  11   . 
     The user client device  108  is optionally associated with a user or user account of an e-commerce platform managed by the online content management system  104 . For instance, the user client device  108  is associated with a consumer of a product. Additionally, the user client device  108  is optionally associated with a user who is browsing and viewing products listed by the online content management system  104 . As mentioned, the user client device  108  communicates with the server device(s)  102 . In particular, the user client device  108  uploads and sends digital data including digital images (e.g., user-submitted images) to the server device(s)  102  via the network  112 . Additionally, the user client device  108  displays graphical user interfaces including product images to a user associated with the user client device  108 . 
     Additionally, or alternatively, the user client device  108  (or another client device) is associated with a seller of a product or a marketer of a product. For example, the user client device  108  sends, to the server device(s)  102  information regarding products for sale by the seller including digital images portraying the product for sale. In some embodiments, the user client device  108  sends, to the server device(s)  102 , authentic digital images portraying authentic products. In some examples the user client device  108  is associated authentic vendor or seller. The authentic vendor sends, to the device(s)  102  via the user client device  108 , authentic graphic features that the counterfeit identification system  106  uses to evaluate digital images potentially containing counterfeit products. 
     As illustrated in  FIG.  1   , the user client device  108  includes the application  110 . The application  110  may be a web application or a native application on the user client device  108  (e.g., a mobile application, a desktop application, etc.). The application  110  interfaces with the counterfeit identification system  106  to provide digital content including product information such as digital images to the device(s)  102 . In one or more implementations, the application  110  is a browser that renders a graphical user interface on the display of the user client device  108 . For example, the application  110  renders a series of graphical user interfaces for uploading product information and managing associations between product information and promotional content. Additionally, the application  110  optionally presents simulations of web pages from a perspective accessing the web page from a customer client device. Simulating the web pages to preview content regarding the product allows a seller to review the product information. Furthermore, in some embodiments, the application  110  renders an indication of whether a product has been flagged as a counterfeit or authentic product. 
     Although  FIG.  1    depicts the counterfeit identification system  106  located on the device(s)  102 , in some embodiments, the counterfeit identification system  106  is implemented by (e.g., located entirely or in part) on one or more other components of the environment  100 . For example, the counterfeit identification system  106  may be implemented entirely (or in part) on the user client device  108 . For example, the device(s)  102  and/or the user client device  108  has digital images or authentic digital images stored thereon. 
     Although the environment  100  includes a single user client device  108 , in one or more embodiments, the environment  100  includes multiple user client devices and client devices. For example, the environment  100  include a first user client device  108  associated with a buyer who views a web page displaying product information for the product. The environment  100  also optionally includes a second user client device  108  associated with a seller or vendor who has uploaded product information including digital images and/or authentic digital images. 
     Additionally, the user client device  108  optionally communicates directly with the counterfeit identification system  106 , bypassing the network  112 . Moreover, counterfeit identification system  106  is able to access one or more databases (e.g., a digital image database) housed on the server device(s)  102  or elsewhere in the environment  100 . Further, the counterfeit identification system  106  optionally includes one or more machine learning models (e.g., neural networks), and the counterfeit identification system  106  is implemented in a variety of different ways across the server device(s)  102 , the network  112 , and the user client device  108 . 
     In particular, in some implementations, the counterfeit identification system  106  on the server device(s)  102  supports the application on the user client device  108 . For instance, the counterfeit identification system  106  on the server device(s)  102  generates or trains the counterfeit identification system  106 . The server device(s)  102  provides the trained counterfeit identification system  106  to the user client device  108 . In other words, the user client device  108  obtains (e.g., downloads) the counterfeit identification system  106  from the server device(s)  102 . At this point, the user client device  108  is able to utilize the counterfeit identification system  106  to replace to detect digital images of counterfeit goods independently from the server device(s)  102 . 
     In alternative embodiments, the counterfeit identification system  106  includes a web hosting application that allows the user client device  108  to interact with content and services hosted on the server device(s)  102 . To illustrate, in one or more implementations, the user client device  108  accesses a web page supported by the server device(s)  102 . The user client device  108  provides input to the server device(s)  102  to perform counterfeit detection, and, in response, the counterfeit identification system  106  on the server device(s)  102  performs operations. The server device(s)  102  then provides the output or results of the operations to the user client device  108 . 
     While  FIG.  1    illustrates an example environment in which the counterfeit identification system  106  operates, the following figures and corresponding discussion provide additional detail regarding how the counterfeit identification system  106  determines that digital images portray counterfeit products in accordance with one or more embodiments. For example,  FIG.  2    illustrates a general overview of the counterfeit identification system  106  determining that a digital image portrays a counterfeit product in accordance with one or more embodiments. In particular,  FIG.  2    illustrates the counterfeit identification system  106  generating and comparing authentic graphic features from an authentic digital image with graphic features from a digital image to determine that the digital image portrays a counterfeit product. More specifically,  FIG.  2    illustrates a series of acts  200  comprising an act  202  of extracting an authentic graphic element, an act  204  of determining authentic graphic features, an act  206  of extracting a graphic element, an act  208  of generating graphic features, and an act  210  of determining that the digital image portrays a counterfeit product. 
     As illustrated in  FIG.  2   , the series of acts  200  includes the act  202  of extracting an authentic graphical element. In particular, the act  202  comprises extracting an authentic graphic element  216  from an authentic digital image  212 . In some embodiments, and as illustrated, the authentic digital image  212  comprises a digital image portraying an authentic product (e.g., a t-shirt). In other embodiments, the authentic digital image  212  comprises a digital image of the authentic graphic element  216 . For example, in some embodiments, the authentic digital image comprises an authentic graphical element digital image  214 . 
     The counterfeit identification system  106  receives the authentic digital image  212  and/or the authentic graphical element digital image  214  from a client device associated with an authentic vendor. For example, an authentic vendor owns the authentic graphic element  216 . Additionally, or alternatively, the authentic vendor is a third party authorized by the organization to use the authentic graphic element  216 . Authentic vendors typically have authentic graphical element digital images (e.g., logo files) readily available, and the counterfeit identification system  106  receives and stores the authentic graphical element digital images in a repository of authentic graphic elements. In some embodiments, the counterfeit identification system  106  extracts the authentic graphic element  216  by accessing the repository of authentic graphical elements. 
     In instances where the counterfeit identification system  106  receives an authentic digital image  212  instead of the authentic graphical element digital image  214 , the counterfeit identification system  106  extracts the authentic graphic element  216  from the authentic digital image  212 . In particular, the counterfeit identification system  106  utilizes a machine learning model to generate a bounding box including a boundary of the authentic graphic element  216 , a mask region, and a confidence score.  FIG.  3    and the corresponding paragraphs describe how the counterfeit identification system  106  utilizes the machine learning model to extract the authentic graphic element  216  in accordance with one or more embodiments. 
     As further illustrated in  FIG.  2   , the series of acts  200  includes the act  204  of determining authentic graphic features. In particular, the act  204  comprises determining authentic graphic features for the authentic graphic element  216 , wherein the authentic graphic features comprise authentic shape features  218 , authentic color features  220 , and authentic text features  222 . 
     Additionally, in some embodiments, the counterfeit identification system  106  performs the act  204  by generating the authentic shape features  218  based on analyzing the authentic graphic element  216 . In particular, the counterfeit identification system  106  generates the authentic shape features  218  by generating an edge map of the authentic graphic element  216  utilizing a canny edge filter together with adaptive hysteresis.  FIG.  4    and the corresponding discussion provide additional detail regarding how the counterfeit identification system  106  determines the authentic shape features  218  in accordance with one or more embodiments. 
     As further illustrated in  FIG.  2   , the counterfeit identification system  106  generates the authentic color features  220  based on analyzing the authentic graphic element  216 . Generally, the counterfeit identification system  106  analyzes pixels within the authentic graphic element  216  to determine valid color shades and gradients of the authentic graphic element  216 . For example, the counterfeit identification system  106  maps color values for each pixel within the authentic graphic element  216  in a color space, such as an HSV color space. The counterfeit identification system  106  then utilizes the resulting values as the authentic color features  220 . In some embodiments, the authentic color features  220  comprise a valid range of color values for each pixel location. In particular, the counterfeit identification system  106  accounts for variation in color due to lighting conditions in which digital images are captured by determining the valid range of color values.  FIG.  5    and the corresponding paragraphs provide additional detail regarding how the counterfeit identification system  106  determines the authentic color features  220  in accordance with one or more embodiments. 
     As part of performing the act  204  illustrated in  FIG.  2   , the counterfeit identification system  106  also analyzes the authentic graphic element  216  to generate the authentic text features  222 . In particular, the counterfeit identification system  106  extracts text and its style used within the authentic graphic element  216 . For example, the counterfeit identification system  106  analyzes properties of the text including position, style, warp, and color of the text within the authentic graphic element  216 .  FIG.  6    and the accompanying paragraphs provide additional detail relating to how the counterfeit identification system  106  generates the authentic text features  222  in accordance with one or more embodiments. 
     The series of acts  200  illustrated in  FIG.  2    also includes the act  206  of extracting a graphic element. In particular, the act  206  comprises extracting a graphic element  228  from a digital image  226  portraying a product. The counterfeit identification system  106 , optionally, utilizes the same machine learning model utilized as part of the act  202  of extracting an authentic graphic element to extract the graphic element  228 . For example, in some embodiments, the counterfeit identification system  106  utilizes a Convolutional Neural Network (CNN)-based model that is trained using various graphic element images.  FIG.  3    and the corresponding paragraphs provide additional detail relating to how the counterfeit identification system  106  extracts the graphic element  228  in accordance with one or more embodiments. 
     As further illustrated in  FIG.  2   , the series of acts  200  also includes the act  208  of generating graphic features. In particular, the counterfeit identification system  106  generates graphic features for the graphic element  228 , wherein the graphic features comprise shape features  230 , color features  232 , and text features  234 . In some embodiments, the counterfeit identification system  106  analyzes the graphic element  228  utilizing methods and processes similar to how the counterfeit identification system  106  analyzes the authentic graphic element  216 . For example, the counterfeit identification system  106  generates the shape features  230  by utilizing a canny edge detector and, optionally, adaptive hysteresis. The counterfeit identification system  106  also generates the color features  232  by mapping color values for each pixel within the graphic element  228  in a color space. The counterfeit identification system  106  further generates the text features  234  by analyzing text properties of text within the graphic element  228 , wherein the text properties include position, style, warp, and color.  FIGS.  3 - 6    and the corresponding paragraphs provide additional detail regarding how the counterfeit identification system  106  generates the shape features  230 , the color features  232 , and the text features  234  in accordance with one or more embodiments. 
     The series of acts  200  illustrated in  FIG.  2    further includes the act  210  of determining that the digital image portrays a counterfeit product. In particular, the counterfeit identification system  106  compares authentic shape features with the shape features, authentic color features with the color features, and authentic text features with the text features. In some embodiments, the counterfeit identification system  106  determines that the digital image portrays a counterfeit product based on determining that at least one of the shape features, color features, or text features are different than the authentic shape features, authentic color features, and authentic text features, respectively. Furthermore, in some embodiments, the counterfeit identification system  106  determines that the digital image portrays a counterfeit product based on any one of the graphic features is outside an authentic graphic feature range. For example, the counterfeit identification system  106  determines that the digital image portrays a counterfeit product based on determining that the color features are outside an authentic color feature range. 
     Additionally, or alternatively, the counterfeit identification system  106  performs the act  210  by utilizing a counterfeit detection model. In particular, the counterfeit identification system  106  may utilize the counterfeit detection model to analyze graphic features and authentic graphic features. Based on analyzing the graphic features and the authentic graphic features, the counterfeit identification system  106  utilizes the counterfeit detection model to generate similarity confidence score. Based on the similarity confidence score falling below a threshold similarity value, the counterfeit identification system  106  determines that the digital image portrays a counterfeit product.  FIGS.  8 A- 8 B  illustrate the counterfeit identification system  106  learning parameters for and applying a counterfeit detection model in accordance with one or more embodiments. 
       FIG.  2    and the accompanying paragraphs describe an overview of the counterfeit identification system  106  determining that a digital image portrays a counterfeit product in accordance with one or more embodiments. The following figures and paragraphs further detail how the counterfeit identification system  106  performs the acts described with respect to  FIG.  2   . In particular,  FIG.  3    illustrates how the counterfeit identification system  106  utilizes a region proposal neural network  330  to locate and identify a graphic element  346  in a digital image  334 . 
     As mentioned previously, in some embodiments, the counterfeit identification system  106  utilizes a machine learning model to identify and locate the graphic element  346  within the digital image  334 . The digital image  334  may represent a digital image received by the counterfeit identification system  106  from a user client device. The digital image  334  comprises a digital image portraying either an authentic or counterfeit product. For example, in some embodiments, the digital image  334  comprises a digital image portraying a counterfeit product having the counterfeit graphic element  346 . In another example, the digital image  334  comprises an authentic digital image portraying an authentic product from which the counterfeit identification system  106  extracts an authentic graphic element. 
       FIG.  3    illustrates one implementation of a machine learning model that the counterfeit identification system  106  can utilize. In particular,  FIG.  3    illustrates a region proposal neural network  330  in accordance with one or more implementations. In general, the region proposal neural network  330  can detect objects in images. In one or more embodiments, the region proposal neural network  330  is a deep learning convolutional neural network (CNN). For example, in some embodiments, the region proposal neural network  330  is a region-based CNN (R-CNN) or an object detection neural network. For instance, an example of a region proposal neural network is found in U.S. Patent Application Publication No.  2021 / 0027083  and entitled Automatically Detecting User-Requested Objects In Images, the entire contents of which are hereby incorporated by reference in their entirety. Another example region proposal neural network is found in S. Ren, K. He, R. Girshick, and J. Sun, Faster R-CNN.  Towards real - time object detection with region proposal networks,  NIPS, 2015, the entire contents of which is hereby incorporated by reference. 
     While  FIG.  3    illustrates one implementation of a region proposal neural network, the counterfeit identification system  106  utilizes alternative machine learning models such as an object detection neural network or a logo detection neural network. An example, object detection neural network is described in U.S. patent application Ser. No. 17/038,866, filed on Sep. 30, 2020 and entitled Generating Composite Images With Objects From Different Times, the entire contents of which are hereby incorporated by reference in their entirety. An example, logo detection neural network, called a machine-learning-logo classifier, is described in U.S. Pat. No. 11,106,944, entitled Selecting Logo Images Using Machine-Learning-Logo Classifiers, the entire contents of which are hereby incorporated by reference in their entirety. 
     As shown in  FIG.  3   , the region proposal neural network  330  includes lower neural network layers  338  and higher neural network layers  340 . In general, the lower neural network layers  338  collectively form an encoder and the higher neural network layers  340  collectively form a decoder (or potential object detector). In one or more embodiments, the lower neural network layers  338  are convolutional layers that encode the digital image  334  into feature vectors, which are outputted from the lower neural network layers  338  and inputted to the higher neural network layers  340 . In various implementations, the higher neural network layers  340  comprise fully-connected layers that analyze the feature vectors and output the graphic element proposals  342  (e.g., bounding boxes around potential objects) and the proposal confidence scores  344 . 
     In particular, the lower neural network layers  338  comprise convolutional layers that generate a feature vector in the form of a feature map. To generate the graphic element proposals  342 , the region proposal neural network  330  processes the feature map utilizing a convolutional layer in the form of a small network that is slid across small windows of the feature map. The region proposal neural network  330  then maps each sliding window to a lower-dimensional feature. The region proposal neural network  330  then processes this feature using two separate heads that are fully connected layers. In particular, the first head optionally comprises a box-regression layer that generates the graphic element proposals  342  and a box-classification layer that generates the proposal confidence scores  344 . As noted above, for reach region proposal, the region proposal neural network  330  generates a corresponding proposal confidence scores  344 . 
     Furthermore, in some embodiments, the graphic element proposals  342  comprises a bounding box including the boundary of the graphic element  346  together with a mask region. Generally, mask regions indicates the borders of an object within a digital image. In particular, the region proposal neural network  330  comprises a Mask R-CNN that has an additional branch for predicting segmentation masks for each graphic element proposal in a pixel-to-pixel manner. In particular, the Mask R-CNN includes the region proposal neural network  330  to propose candidate graphic element bounding boxes and a binary mask classifier to generate masks for every class. The counterfeit identification system  106  may utilize the region proposal neural network  330  to generate multiple Regions of Interest (RoI). The counterfeit identification system  106  may then utilize a RoI Align network to warp the RoI into a fixed dimension. The counterfeit identification system  106  further feeds the warped features into fully connected layers to make classifications. The warped features are also fed into the binary mask classifier which consists of one or more CNNs to output a binary mask for each RoI. 
     The counterfeit identification system  106  may utilize the region proposal neural network  330  to generate a plurality of candidate graphic elements and their corresponding candidate bounding boxes, candidate mask regions, and candidate confidence scores. For example, a product depicted within the digital image  334  optionally includes several different designs, words, and symbols. In some embodiments, the counterfeit identification system  106  utilizes the confidence scores to select the graphic element  346  from the candidate bounding boxes. For example, the counterfeit identification system  106  designates a candidate graphic element corresponding with the highest candidate confidence score as the graphic element  346 . 
     Furthermore, in some embodiments, the counterfeit identification system  106  selects the graphic element  346  based on a proposal confidence score meeting a proposal confidence score threshold. Generally, the counterfeit identification system  106  will not classify a digital image as portraying an authentic or counterfeit product if none of the proposal confidence scores  344  associated with the graphic element proposals  342  meet the proposal confidence score threshold. For example, based on determining that none of the proposal confidence scores  344  meet the proposal confidence score threshold, the counterfeit identification system  106  labels the digital image  334  as being unclassified or unknown. 
       FIG.  3    and the corresponding paragraphs above describe the counterfeit identification system  106  utilizing a machine learning model to extract a graphic element from a digital image in accordance with one or more embodiments. As mentioned previously, the counterfeit identification system  106  generates graphic features from extracted graphic elements. The following figures and paragraphs further detail how the counterfeit identification system  106  generates shape features, color features, and text features for both authentic graphic elements and graphic elements in accordance with one or more embodiments. In particular,  FIG.  4    and the accompanying paragraphs detail how the counterfeit identification system  106  generates authentic shape features for authentic graphic elements and shape features for graphic elements in accordance with one or more embodiments. To illustrate,  FIG.  4    illustrates a series of acts  400  including an act  402  of generating an edge image, an act  404  of generating a shape representation, and an optional act  406  of normalizing the shape representation. 
     As mentioned, the counterfeit identification system  106  generates authentic color features for an authentic graphic element and color features for a graphic element within a digital image. The counterfeit identification system  106  may perform the series of acts  400  as part of generating both the authentic color features and the color features. In some embodiments, the counterfeit identification system  106  performs the optional act  406  of normalizing the shape representation for the authentic graphic element but not for the graphic element. In yet other embodiments, the counterfeit identification system  106  performs all of the acts within the series of acts  400  for both the authentic graphic element and the graphic element. 
     As illustrated in  FIG.  4   , the series of acts  400  includes the act  402  of generating an edge image. In particular, the counterfeit identification system  106  may generate an edge image  414  for a graphic element  408  by utilizing a canny edge detector  410  and, optionally, adaptive hysteresis  412 . As mentioned previously, the graphic element  408  represents an authentic graphic element or a graphic element within a digital image. Furthermore, the graphic element  408  comprises a bounding box including the boundaries of a graphic element output by the region proposal neural network described above in relation to  FIG.  2   . 
     As part of performing the act  402  of generating an edge image, the counterfeit identification system  106  may utilize the canny edge detector  410 . Generally, the canny edge detector  410  comprises an edge detection algorithm that detects a wide range of edges in images. For example, the counterfeit identification system  106  utilizes the canny edge detector  410  to generate the edge image  414  indicating both boundaries and internal edges of the graphic element  408 . In some embodiments, the counterfeit identification system  106  utilizes a different type of edge detection algorithm in place of the canny edge detector  410 . For example, the counterfeit identification system  106  may utilize a Deriche edge detection algorithm or various other edge detection algorithms. In some embodiments, the counterfeit identification system  106  utilizes the canny edge detector  410  to generate the edge image  414 . 
     In some embodiments the counterfeit identification system  106  additionally utilizes adaptive hysteresis  412  to compute the edge image  414 . Generally, the counterfeit identification system  106  utilizes adaptive hysteresis  412  to capture minor boundary details within the graphic element  408 . In particular, adaptive hysteresis  412  includes computing an adaptive threshold while performing canny edge detection. The use of adaptive hysteresis  412  enables the counterfeit identification system  106  to produce clear boundaries or edges for the edge image  414 . For example, adaptive hysteresis  412  counters streaking commonly found in edge images generated using a single threshold limit. More specifically, in systems utilizing a single threshold limit, pixels meeting the threshold limit will contain an edge while pixels falling below the threshold limit will not have an edge. In contrast, in one or more embodiments, adaptive hysteresis  412  processes pixel values using an upper threshold limit and a lower threshold limit. The counterfeit identification system  106  designates pixels with vales over the upper threshold limit as edge pixels and does not designate pixels having values below the lower threshold limit as edge pixels. The counterfeit identification system  106  determines that pixels with values between the upper threshold limit and the lower threshold limit are edge pixels when surrounding pixels have already been designated as edge pixels or also exhibit high pixel values. Thus, the use of adaptive hysteresis  412  reduces streaking in the edge image  414 . 
     The series of acts  400  illustrated in  FIG.  4    includes the act  404  of generating a shape representation. In particular, the counterfeit identification system  106  generates a shape representation  418  that indicates a collection of pixel coordinates corresponding to the edge image  414 . In some embodiments, the counterfeit identification system  106  determines an edge value for each pixel location for the edge image  414 . For instance, and as illustrated in  FIG.  4   , the counterfeit identification system  106  determines a binary edge value (0 or 1) for each pixel location based on whether the pixel location has an edge. The edge values are portrayed using a matrix of edge values  416 . Each square in the matrix of edge values  416  corresponds with a pixel location in the edge image  414 . The counterfeit identification system  106  then generates the shape representation  418  by creating a list of pixel coordinates corresponding having a positive (e.g., 1) edge value. 
     As mentioned, the counterfeit identification system  106  may perform the optional act  406  of normalizing the shape representation. Generally, the counterfeit identification system  106  performs the optional act  406  to make the shape representations comparable to each other. For example, the dimensions of an authentic graphic element (e.g., 400 pixels×400 pixels) may be different than the dimensions of a graphic element (e.g., 200 pixels×200 pixels). For example, in some embodiments, the counterfeit identification system  106  converts the pixel coordinates of a single pixel into the normalized coordinates (x, y). In one or more embodiments, the counterfeit identification system  106  normalizes the pixel coordinates to a range between 0 and 1. 
     For example, the dimensions of the edge image  414  are 400 px by 400 px. The counterfeit identification system  106  converts the pixel coordinates within the shape representation  418  into normalized coordinates falling between 0 and 1. In some embodiments, the counterfeit identification system  106  does so by dividing the x pixel coordinate by the dimension width and the y coordinate by the dimension height. As illustrated, the normalized pixel coordinates for the shape representation equals (0.875, 0.225). 
     In some embodiments, the counterfeit identification system  106  performs the optional act  406  for authentic graphic elements but not for other graphic elements. For instance, in some embodiments, the counterfeit identification system  106  determines normalized authentic shape representations that the counterfeit identification system  106  uses to compare with shape representations corresponding with graphic elements within digital images. In other embodiments, the counterfeit identification system  106  normalizes shape representations for both authentic graphic elements and other graphic elements for comparison with each other. 
     Furthermore, the counterfeit identification system  106  may determine to perform the optional act  406  based on the type of file of the authentic graphic element. Generally, the counterfeit identification system  106  may receive the graphic element  408  in two forms: vector form and raster form. In particular, a graphic element in vector form comprises a vector file that is built by mathematical formulas that establish points on a grid. Vector file types include EPS, AI, PDF, and other file types. If the graphic element  408  is in vector form, the counterfeit identification system  106  may easily resize the graphic element  408  for comparison with other graphic elements. 
     In contrast, the counterfeit identification system  106  may determine to perform the optional act  406  of normalizing the shape representation based on determining that the graphic element  408  is in raster form. In particular, a graphic element in raster form comprises a raster file comprising many colored pixels or individual building blocks. Raster file types include JPEGs, GIFs, PNGs, and other image types. If the graphic element  408  is in raster form, the counterfeit identification system  106  may perform the optional act  406  of normalizing the shape representation. 
       FIG.  4    illustrates how the counterfeit identification system  106  generates authentic shape features and shape features in accordance with one or more embodiments. As mentioned, the counterfeit identification system  106  may also generate authentic color features and color features.  FIG.  5    and the corresponding paragraphs further detail how the counterfeit identification system  106  generates authentic color features and color features in accordance with one or more embodiments. In particular,  FIG.  5    illustrates a series of acts  500  comprising an act  502  of normalizing pixel coordinates and an act  504  of mapping pixels from the graphic element into a color space. As just mentioned, the counterfeit identification system  106  may perform the series of acts  500  as part of generating authentic color features associated with an authentic graphic element and also generating color features associated with a digital image. 
     As illustrated in  FIG.  5   , the series of acts  500  includes the act  502  of normalizing pixel coordinates. Generally, the counterfeit identification system  106  performs the act  502  to make the color features comparable to each other. As mentioned previously in relation to normalizing shape representations, the counterfeit identification system  106  converts the pixel coordinates of a single pixel into normalized coordinates (x, y). In one or more embodiments, the counterfeit identification system  106  normalizes the pixel coordinates to values within a range between 0 and 1. The counterfeit identification system  106  may normalize pixel coordinates based on the authentic graphic element being in raster form as opposed to an authentic graphic element being in vector form. Furthermore, in one or more embodiments, the counterfeit identification system  106  normalizes pixel coordinates for the authentic graphic element but not the graphic element. In yet other embodiments, the counterfeit identification system  106  normalizes pixel coordinates for both authentic graphic elements and graphic elements portrayed in digital images. 
     As further illustrated, in  FIG.  5   , the series of acts  500  includes the act  504  of mapping pixels from the graphic element into a color space. In particular, the counterfeit identification system  106  maps pixels from a graphic element  510  into a color space to generate color features. The graphic element  510  represents an authentic graphic element or a graphic element portrayed within a digital image. To illustrate, the counterfeit identification system  106  maps a pixel  506   a  at a first pixel location and a pixel  506   b  at a second pixel location into a color space  512 . The counterfeit identification system  106  utilizes the color space  512  to generate color values for each of the pixels  506   a - 506   b.    
     The counterfeit identification system  106  is able to utilize a variety of color spaces as part of generating color features and authentic color features. In one or more embodiments, the counterfeit identification system  106  maps the pixels  506   a - 506   b  into a Hue, Saturation, Value (HSV) or Hue, Saturation, Lightness (HSL) color space. The HSV and HSL color spaces are alternative representations of an RGB color model. In the HSV color space, hue is expressed as a value from 0 to 360 degrees. Saturation describes the amount of gray in a particular color and are optionally expressed as a value between 0 and 100 where 0 is associated with gray and produces a faded effect. Value, or brightness, describes the brightness or intensity of a color and are optionally expressed as a value between 0 and 100 percent where 0 is completely black and 100 is the brightest and reveals the most color. While  FIG.  5    illustrates the counterfeit identification system  106  mapping the pixels  506   a - 506   b  in an HSV color space, the counterfeit identification system  106  may map the pixels  506   a - 506   b  in alternate color spaces. For example, the counterfeit identification system  106  maps the pixels  506   a - 506   b  in a Red Yellow Blue (RYB), Red Green Blue (RBG), a Cyan Magenta Yellow (CMY), or other color space. 
     As illustrated in  FIG.  5   , the counterfeit identification system  106  maps the pixels from the graphic element  510  into a color space to generate color values  508   a - 508   b.  As illustrated in  FIG.  5   , the color values  508   a - 508   b  correspond with the pixels  506   a - 506   b,  respectively. The counterfeit identification system  106  further associates the normalized pixel coordinates with the color values  508   a - 508   b.  In one or more embodiments, the color features and the authentic color features comprise a matrix of color values. In particular, each position in the matrix of color values corresponds with a pixel location within the graphic element  510 . Each position may correspond with one or more different color values. For example, if the pixels from the graphic element were mapped into an HSV color space, each position in the matrix of color values is associated with a hue value, a saturation value, and a brightness value. 
       FIG.  5    and the accompanying paragraphs describe how the counterfeit identification system  106  generates color features and authentic color features in accordance with one or more embodiments. The counterfeit identification system  106  may also generate authentic text features and text features.  FIG.  6    and the corresponding paragraphs illustrate how the counterfeit identification system  106  generates authentic text features and text features in accordance with one or more embodiments. In particular,  FIG.  6    illustrates a series of acts  600  including an act  602  of identifying text within the graphic element, an act  604  of extracting text properties, and an act  606  of maintaining a hash map. 
     As illustrated in  FIG.  6   , the series of acts  600  includes the act  602  of identifying text within the graphic element. In particular, the counterfeit identification system  106  utilizes a text detection machine learning model  610  to extract text  612  from a graphic element  608 . The graphic element  608  comprises an authentic graphic element or a graphic element from a digital image. In some embodiments, text detection machine learning model  610  comprises a CNN-based model trained using text having various properties. The counterfeit identification system  106  analyzes an image utilizing the text detection machine learning model  610  and generates a bounding box  616  including the text  612  and a corresponding text confidence score indicating a likelihood that the bounding box  616  includes text. Furthermore, in some embodiments, the text detection machine learning model  610  generates character bounding boxes  614  for individual characters within the text  612 . The text detection machine learning model  610  also generates character confidence scores corresponding to the character bounding boxes  614  that indicate the likelihood that a given character bounding box contains a character. 
     The series of acts  600  illustrated in  FIG.  6    also includes the act  604  of extracting text properties. In particular, the counterfeit identification system  106  analyzes the text  612  to extract text properties comprising at least one of position, style, warp, and color. In particular, the counterfeit identification system  106  analyzes the text  612  to extract a text position. In some embodiments, the counterfeit identification system  106  determines the position of the text  612  relative to the graphic element  608 . For example, the counterfeit identification system  106  determines normalized pixel coordinates for the top left position (XY) of the text  612  within the graphic element  608 . Additionally, or alternatively, the counterfeit identification system  106  may also determine the size of the text  612  relative to the graphic element  608 . For example, the counterfeit identification system  106  determines normalized pixel coordinates corresponding with boundaries of the text  612 . 
     As further illustrated in  FIG.  6   , the counterfeit identification system  106  also determines a text style as part of extracting text properties. Generally, text style is a resource that specifies stylistic text attributes. Example text styles include bold, italic, underline, strikethrough, overlining, capitalization, normal, or other text styles. 
     The counterfeit identification system  106  may also determine text warp as part of extracting text properties. Text warp refers to design or distortions that are applied to the text  612  to change the shape of the text  612 . Examples of text warp properties include arc, bulge, inflation, fisheye, and other distortions. 
     As further illustrated in  FIG.  6   , as part of performing the act  604  of extracting text properties, the counterfeit identification system  106  also determines text color. In particular, the counterfeit identification system  106  determines the color of the text  612 . In some embodiments, the counterfeit identification system  106  determines the color of the text  612  by mapping pixels within the text  612  in a color space (e.g., RGB, HSV, etc.). In certain embodiments, the counterfeit identification system  106  reduces the compute load required to extract text colors by selecting specific pixels within the text  612  for which to determine color values. For example, the counterfeit identification system  106  determines a color value (or range) for a pixel located in the top left of the text  612  and a second color value (or range) for a pixel located in the bottom right of the text  612 . Additionally, or alternatively, the counterfeit identification system  106  measures a text color variation from one point in the text  612  to another point in the text  612 . For example, the counterfeit identification system  106  determines a difference in color between a pixel located in the top left of the text  612  and a pixel located in the bottom right of the text  612 . 
     Furthermore, as illustrated in  FIG.  6   , the counterfeit identification system  106  determines a font of the text  612  as part of performing the act  604  of extracting text properties. Generally, font indicates a particular size, weight, and style of a typeface. In particular, a font comprises a matched set of type with a piece for each glyph and a typeface consisting of a range of fonts that share an overall design. For example, the counterfeit identification system  106  determines that the text  612  is in Times New Roman, EB Garamond, Lora, or other font. 
     Additionally, in some embodiments, the counterfeit identification system  106  further extracts text kerning as part of performing the act  604 . Generally, kerning refers to the spacing between characters in a text. The counterfeit identification system  106  determines the center of each character delineated by the character bounding boxes  614 . The counterfeit identification system  106  then measures distances between the centers of each character. For example, the counterfeit identification system  106  measures a distance between the center of the “B” and “R,” the “R” and “A,” etc. The counterfeit identification system  106  further determines normalized character distances between each character in the text  612 . For instance, the counterfeit identification system  106  normalizes the character distance relative to the text length. 
     As further illustrated in  FIG.  6   , the series of acts  600  also includes the act  606  of maintaining a hash map. In some embodiments, the counterfeit identification system  106  performs the act  606  only when extracting authentic text properties and not when generating text properties. Generally, the counterfeit identification system  106  maintains a hash map that stores items with the various extracted authentic text properties. In particular, the counterfeit identification system  106  maps an authentic brand and its corresponding authentic graphic element(s) to their authentic text properties within a hash map. Thus, the counterfeit identification system  106  efficiently accesses and references authentic text properties when analyzing digital images. 
     While  FIG.  6    illustrates one way the counterfeit identification system  106  generates authentic text features, in some embodiments, the counterfeit identification system  106  receives authentic text features from a user client device associated with an authentic vendor. For example, the counterfeit identification system  106  receives, from the user client device associated with an authentic vendor, one or more acceptable positions, text styles, text warps, and text color. 
     The preceding figures and paragraphs detail how the counterfeit identification system  106  extracts authentic graphic features and graphic features in accordance with one or more embodiments. As previously mentioned, the counterfeit identification system  106  compares the authentic graphic features with the graphic features to determine whether a graphic element is authentic or counterfeit.  FIGS.  7 A- 7 B  and the corresponding discussion describe how the counterfeit identification system  106  compares the authentic graphic features with the graphic features to determine that a digital image portrays a counterfeit product in one or more embodiments. In particular,  FIGS.  7 A- 7 B  illustrate a series of acts  700  including an act  702  of aligning the authentic graphic element and the graphic element in the same orientation plane, an act  704  of scaling the authentic graphic element bounding box to the graphic element bounding box, an act  706  of comparing the authentic shape features with the shape features, an act  708  of determining an authentic color feature range, an act  710  of comparing the color feature with the authentic color feature range, and an act  712  of comparing authentic text features with text features. 
     As illustrated in  FIG.  7 A , the series of acts  700  includes the act  702  of aligning the authentic graphic element and the graphic element in the same orientation plane. Generally, a graphic element  716  in the digital image may be askew relative to an authentic graphic element  714 . Thus, the counterfeit identification system  106  adjusts the alignment of the graphic element  716  to match that of the authentic graphic element  714 . In some embodiments, the counterfeit identification system  106  turns or orients the graphic element  716  to be in the same orientation plane as the authentic graphic element  714 . In another example, the counterfeit identification system  106  turns both the authentic graphic element  714  and the graphic element  716  to be aligned in a common orientation plane. 
     The series of acts  700  further includes the act  704  of scaling the authentic graphic element bounding box to the graphic element bounding box. Generally, the counterfeit identification system  106  scales an authentic graphic element bounding box  718  to the size of a graphic element bounding box  720 . This is required to normalize the bounding boxes so that comparison happens at the same scale. As mentioned, the counterfeit identification system  106  may simply scale the authentic graphic element bounding box  718  if the authentic graphic element  714  is in vector form. However, if the authentic graphic element  714  is in pixel form, the counterfeit identification system  106  scales the authentic graphic element bounding box  718  based on normalized pixel coordinates. In some embodiments, rather than scaling the authentic graphic element bounding box  718  to match the size of the graphic element bounding box  720 , the counterfeit identification system  106  scales both the authentic graphic element bounding box  718  and the graphic element bounding box  720  to a common normalized scale. For example, the counterfeit identification system  106  adjusts the sizes of the authentic graphic element bounding box  718  and the graphic element bounding box  720  so pixel coordinates within both fall between the values of 0 and 1. In any case, the counterfeit identification system  106  makes the authentic graphic element bounding box  718  and the graphic element bounding box  720  the same size for comparison. 
     The counterfeit identification system  106  performs the acts  706 - 712  as part of comparing the graphic features and the authentic graphic features. In particular, and as illustrated in  FIG.  7 A , the counterfeit identification system  106  performs the act  706  of comparing the authentic shape features with the shape features. In particular, the act  706  comprises comparing an authentic shape representation  722  with a shape representation  724 . As described previously, the authentic shape representation  722  comprises a list of pixel coordinates corresponding with edge pixels of the authentic graphic element  714 , and the shape representation  724  comprises a list of pixel coordinates corresponding with edge pixels of the graphic element  716 . Furthermore at least one of the authentic shape representation  722  and the shape representation  724  comprise normalized pixel coordinates for comparison on the same scale. 
     In some embodiments, the counterfeit identification system  106  performs the act  706  of comparing the authentic shape features with the shape features by subtracting the shape representation  724  from the authentic shape representation  722  or vice versa. By subtracting the shape representation  724  from the authentic shape representation  722 , the counterfeit identification system  106  is able to find substantial differences between the shape or edges of the authentic graphic element  714  and the graphic element  716 . For example, the counterfeit identification system  106  determines that the shape representation  724  differs from the authentic shape representation  722  at an edge coordinate  726 . Based on determining that the shape features differ from the authentic shape features, the counterfeit identification system  106  determines that the graphic element  716  is a counterfeit graphic element. In some embodiments, the counterfeit identification system  106  determines that the shape features differ from the authentic shape features based on determining that differences between the features meet a shape difference threshold. For example, the counterfeit identification system  106  determines a sum of shape differences based on the differences between the shape representation  724  and the authentic shape representation  722 . Based on the sum of shape differences meeting a shape difference threshold, the counterfeit identification system  106  determines that the graphic element  716  is a counterfeit graphic element. 
     As illustrated in  FIG.  7 A , the counterfeit identification system  106  further performs the act  708  of determining an authentic color feature range. In some embodiments, the counterfeit identification system  106  determines an authentic color feature range for each pixel coordinate within the authentic graphic element. To illustrate, the counterfeit identification system  106  determines Hue (H), Saturation (S), and Value or brightness (V) values for an authentic pixel coordinate  728  of the authentic graphic element  714 . The counterfeit identification system  106  determines an authentic color feature range to account for variations in lighting, image editing, and other variables in digital images. For example, the counterfeit identification system  106  determines an authentic hue range of 234-240 degrees and an authentic saturation range of 73-76% for the authentic pixel coordinate  728 . In some embodiments, the counterfeit identification system  106  determines authentic color feature ranges for some properties and not others. For example, the counterfeit identification system  106  does not determine an authentic value range but rather utilizes the authentic brightness value of 87% for comparison. 
     In some embodiments, the counterfeit identification system  106  performs the act  708  of determining an authentic color feature range by receiving the authentic color feature range from an authentic vendor. In particular, the counterfeit identification system  106  receives authentic color feature ranges for pixels within the authentic graphic element  714  from a user client device associated with an authentic vendor. In some embodiments, the counterfeit identification system  106  does not receive or determine pixel coordinates corresponding to the authentic color feature ranges. Instead, the counterfeit identification system  106  receives a set of authentic color feature ranges for the whole authentic graphic element  714 . For example, the counterfeit identification system  106  receives a set of authentic color feature ranges acceptable for the authentic graphic element  714 . 
       FIG.  7 A  further illustrates the counterfeit identification system  106  performing the act  710  of comparing the color feature with the authentic color feature range. Generally, the counterfeit identification system  106  determines whether the color feature falls within the authentic color feature range. In particular, the counterfeit identification system  106  compares color features associated with each pixel coordinate within the graphic element  716  with the authentic color features at the corresponding pixel coordinate within the authentic graphic element  714 . To illustrate, the counterfeit identification system  106  determines HSV values for a pixel coordinate  730  in the digital image. The counterfeit identification system  106  compares the HSV values at the pixel coordinate  730  with the HSV value ranges at the authentic pixel coordinate  728 . The counterfeit identification system  106  determines that the hue value at the pixel coordinate  730  (232 degrees) falls outside the authentic hue range (234-240 degrees) of the corresponding authentic pixel coordinate  728 . In some embodiments, based on determining that the color feature falls outside the authentic color feature range, the counterfeit identification system  106  determines that the graphic element  716  is a counterfeit graphic element. 
     As mentioned, in some examples, the counterfeit identification system  106  determines or receives a set of authentic color feature ranges associated with the authentic graphic element  714 . In such instances, the counterfeit identification system  106  compares color features of the graphic element  716  with the set of authentic color feature ranges. To illustrate, the counterfeit identification system  106  selects pixels at various pixel coordinates within the graphic element  716 . The counterfeit identification system  106  compares the color features (e.g., HSV values) at the various pixel coordinates with the set of authentic color feature ranges. Based on determining that one or more of the color features falls outside the set of authentic color feature ranges, the counterfeit identification system  106  flags the graphic element  716  as a counterfeit graphic element. 
     As further illustrated in  FIG.  7 B , the series of acts  700  includes the act  712  of comparing authentic text features with text features. In particular, the counterfeit identification system  106  compares text features  734  of the graphic element  716  with authentic text features  732  of the authentic graphic element  714 . In some embodiments, based on any deviation between the authentic text features  732  and the text features  734 , the counterfeit identification system  106  determines that the product associated with the graphic element  716  is a counterfeit product. For example, based on determining that the text style within the text features  734  differs from the text style within the authentic text features  732 , the counterfeit identification system  106  determines that the graphic element  716  is a counterfeit graphic element. 
     As mentioned, in some embodiments, the counterfeit identification system  106  determines that a product portrayed within a digital image is a counterfeit product based on comparing authentic graphic features with graphic features. In some embodiments, the counterfeit identification system  106  determines that a product is a counterfeit product based on any one of the shape features, color features, or text features deviating from the authentic shape features, authentic color features, or authentic text features, respectively. In other embodiments, the counterfeit identification system  106  determines that a product is a counterfeit product based on any one of the shape features, color features, or text features falling outside an authentic shape feature range, an authentic color feature range, or an authentic text feature range. 
     Furthermore, in some embodiments, the counterfeit identification system  106  utilizes a counterfeit detection model to determine whether a product is authentic or counterfeit. In particular, the counterfeit identification system  106  may utilize a counterfeit detection model to compare the authentic graphic features and the graphic features.  FIGS.  8 A- 8 B  illustrate the counterfeit identification system  106  learning parameters for and utilizing a counterfeit detection model in accordance with one or more embodiments.  FIG.  8 A  illustrates the counterfeit identification system  106  training a counterfeit detection model  804 . Generally, the counterfeit identification system  106  inputs graphic elements  802  and training authentic graphic elements  801  into the counterfeit detection model  804  to generate predicted similarity confidence scores  805 . The counterfeit identification system  106  determines a predicted authenticity  806  based on the predicted similarity confidence scores  805 . The counterfeit identification system  106  compares the predicted authenticity  806  with ground truth authenticity  812  and adjusts the parameters of the counterfeit detection model  804  to reduce a loss  808  between the predicted authenticity  806  and the ground truth authenticity  812 . 
     In relation to  FIG.  8 A , the counterfeit identification system  106  extracts the graphic elements  802  for the counterfeit detection model  804  from training ground truth digital images. In particular, the counterfeit identification system  106  utilizes a CNN to extract the graphic elements  802  from training ground truth digital images. The graphic elements  802  comprise a combination of authentic graphic elements and counterfeit graphic elements associated with authentic digital images and counterfeit digital images, respectively. 
     The counterfeit identification system  106  also inputs training authentic graphic elements  801  into the counterfeit detection model  804 . The authentic graphic elements  801  comprises authentic graphic elements received from authentic vendors. In some embodiments, instead of the authentic graphic elements  801 , the counterfeit identification system  106  inputs authentic shape features, authentic color features, and authentic text features for various authentic graphic elements. 
     The counterfeit identification system  106  utilizes the counterfeit detection model  804  to generate the predicted similarity confidence scores  805 . As part of generating the predicted similarity confidence scores  805 , the counterfeit detection model  804  generates training shape features, training color features, and training text features corresponding to each of the graphic elements  802 . The counterfeit identification system  106  further utilizes the counterfeit detection model  804  to generate the predicted similarity confidence scores  805  corresponding to each of the training shape features, training color features, and training text features. To illustrate, the counterfeit identification system  106  utilizes the counterfeit detection model  804  to generate predicted similarity confidence scores comprising shape similarity scores, color similarity scores, and text similarity scores. The predicted similarity confidence scores  805  indicate how similar the training shape features, the training color features, and the training text features are to authentic shape features, authentic color features, and authentic text features, respectively. 
     As further illustrated in  FIG.  8 A , the counterfeit identification system  106  utilizes the predicted similarity confidence scores  805  to determine the predicted authenticity  806 . In some embodiments, the counterfeit identification system  106  utilizes one or more threshold similarity values as part of generating the predicted authenticity  806 . In particular, based on determining that at least one of the predicted similarity confidence scores  805  falls below a threshold similarity value, the counterfeit identification system  106  determines that the corresponding training digital image portrays a counterfeit product. To illustrate, in some embodiments, the threshold similarity value equals 0.65. Based on determining that any one of the shape similarity score, color similarity score, and text similarity score for a training graphic feature fall below the threshold similarity value, the counterfeit identification system  106  determines that the corresponding training graphic element is a counterfeit graphic element. 
     As mentioned, the counterfeit identification system  106  compares the ground truth authenticity  812  with the predicted authenticity  806 . In particular, the counterfeit identification system  106  determines, evaluates, identifies, or generates a loss  808  between the predicted authenticity  806  and the ground truth authenticity  812 . The counterfeit identification system  106  further adjusts parameters of the counterfeit detection model  804  based on the loss  808 . 
     While  FIG.  8 A  illustrates how the counterfeit identification system  106  learns parameters for the counterfeit detection model  804 ,  FIG.  8 B  illustrates the counterfeit identification system  106  applying the counterfeit detection model  804  in accordance with one or more embodiments. During application, the counterfeit identification system  106  utilizes the counterfeit detection model  804  to analyze a graphic element  814  and authentic graphic element  820 . The counterfeit identification system  106  utilizes the counterfeit detection model  804  to generate similarity confidence scores  822  and makes an authenticity determination  818  based on the similarity confidence scores  822 . 
     The counterfeit identification system  106  extracts the graphic element  814  from a digital image portraying a product. The counterfeit identification system  106  accesses the authentic graphic element  820  from a repository of authentic graphic elements. Additionally, or alternatively, instead of inputting the authentic graphic element  820 , the counterfeit identification system  106  may input authentic graphic features comprising authentic shape features, authentic color features, and authentic text features. 
     The counterfeit identification system  106  utilizes the counterfeit detection model  804  to generate graphic features for the graphic element  814 . In particular, the counterfeit detection model  804  generates graphic features comprising shape features, color features, and text features for the graphic element  814 . The counterfeit detection model  804  further compares the generated graphic features with authentic graphic features from the authentic graphic element  820 . The counterfeit identification system  106  utilizes the counterfeit detection model  804  to further generate the similarity confidence scores  822  based on the comparison of the shape features, color features, and text features. More specifically, the similarity confidence scores  822  comprise a shape similarity score, a color similarity score, and a text similarity score. 
     As previously mentioned, the counterfeit identification system  106  makes the authenticity determination  818  based on the similarity confidence scores  822  and a threshold similarity value. In some embodiments, the threshold similarity value is fixed across all of the similarity confidence scores  822 . For example, based on determining that any one of the shape similarity score, the color similarity score, or the text similarity score falling below a threshold similarity value, the counterfeit identification system  106  determines that the graphic element  814  is a counterfeit graphic element. Additionally, or alternatively, the counterfeit identification system  106  determines individual threshold similarity values for each of the graphic features. For example, the counterfeit identification system  106  determines a threshold shape similarity value of 0.65, a threshold color similarity value of 0.34, and a threshold text similarity value of 0.77. 
     In some embodiments, the counterfeit identification system  106  tunes or adjusts the threshold similarity value. The counterfeit identification system  106  adjusts the threshold similarity value based on the quality of the graphic element  814  or the authentic graphic element  820 . For example, based on determining that the image quality of the graphic element  814  or authentic graphic element  820  is below a resolution threshold, the counterfeit identification system  106  determines to lower the threshold similarity value. In some examples, the counterfeit identification system  106  adjusts the threshold similarity values based on user input. 
       FIG.  9    provides additional detail regarding various components and capabilities of the counterfeit identification system  106  in accordance with one or more embodiments. Generally,  FIG.  9    illustrates the counterfeit identification system  106  implemented by the online content management system  104  on a computing device  900  (e.g., the user client device  108  and/or the server device(s)  102 ). As shown, the counterfeit identification system  106  includes, but is not limited to an authentic digital image manager  902 , a digital image manager  904 , a graphic element extractor  906 , a graphic features comparison machine  908 , a machine learning model manager  910 , and a storage manager  912 . The storage manager  912  stores authentic digital images  914 , digital images  916 , and authentic graphic features  918 . In some embodiments, the counterfeit identification system  106  is implemented as part of the online content management system  104  in a distributed system of the server devices for identifying counterfeit products in digital images. Additionally, or alternatively, the counterfeit identification system  106  is implemented on a single computing device such as the server device(s)  102  of  FIG.  1   . 
     In one or more embodiments, each of the components of the counterfeit identification system  106  are in communication with one another using any suitable communication technologies. Additionally, the components of the counterfeit identification system  106  are in communication with one or more other devices including the user client device  108  illustrated in  FIG.  1   . Although the components of the counterfeit identification system  106  are shown as separate in  FIG.  9   , any of the subcomponents may be combined into fewer components, such as into a single component or divided into more components as may serve a particular implementation. Furthermore, although the components of  FIG.  9    are described in connection with the counterfeit identification system  106 , at least some components for performing operations in conjunction with the counterfeit identification system  106  described herein may be implemented on other devices within the environment. 
     The components of the counterfeit identification system  106  can include software, hardware, or both. For example, the components of the counterfeit identification system  106  can include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices (e.g., the user client device  108 ). When executed by the one or more processors, the computer-executable instructions of the counterfeit identification system  106  can cause the computing devices to perform the object clustering methods described herein. Alternatively, the components of the counterfeit identification system  106  can comprise hardware, such as a special purpose processing device to perform a certain function or group of functions. Additionally, or alternatively, the components of the counterfeit identification system  106  can include a combination of computer-executable instructions and hardware. 
     Furthermore, the components of the counterfeit identification system  106  performing the functions described herein with respect to the counterfeit identification system  106  may, for example, be implemented as part of a stand-alone application, as a module of an application, as a plug-in for applications, as a library function or functions that may be called by other applications, and/or as a cloud-computing model. Thus, the components of the counterfeit identification system  106  may be implemented as part of a stand-alone application on a personal computing device or a mobile device. Alternatively, or additionally, the components of the counterfeit identification system  106  may be implemented in any application that provides image management, including, but not limited to ADOBE® EXPERIENCE CLOUD, such as ADOBE® MAGENTO®, ADOBE® COMMERCE CLOUD, ADOBE® ANALYTICS, ADOBE® MARKETING CLOUDTM, and ADOBE® ADVERTISING CLOUD. “ADOBE”, “ADOBE MAGENTO”, and “ADOBE MARKETING CLOUD” are registered trademarks of Adobe Inc in the United States and/or other countries. 
     The counterfeit identification system  106  includes the authentic digital image manager  902 . The authentic digital image manager  902  receives, accesses, and/or manages authentic digital images portraying authentic products. In some embodiments, the authentic digital image manager  902  manages authentic graphic features received by the counterfeit identification system  106 . 
     As illustrated in  FIG.  9   , the counterfeit identification system  106  also includes the digital image manager  904 . Generally, the digital image manager  904  receives, accesses, and/or manages digital images. 
     The counterfeit identification system  106  also includes the graphic element extractor  906 . The graphic element extractor  906  communicates with the digital image manager  904  to access digital images. The graphic element extractor  906  also utilizes a CNN to extract graphic elements from the digital images. In some embodiments, the graphic element extractor  906  also extracts authentic graphic elements from authentic digital images. 
     The counterfeit identification system  106  illustrated in  FIG.  9    also includes the graphic features comparison machine  908 . The graphic features comparison machine  908  compares authentic graphic features with graphic features. In some embodiments, the graphic features comparison machine  908  manages the counterfeit detection model and utilizes the counterfeit detection model to compare authentic graphic elements with graphic elements. 
       FIG.  9    further illustrates the machine learning model manager  910  as part of the counterfeit identification system  106 . The machine learning model manager  910  manages the various machine learning models utilized by the counterfeit identification system  106 . In some embodiments, the machine learning model manager  910  trains and applies the machine learning models. For example, the machine learning model manager  910  manages and stores the counterfeit detection model, a region proposal neural network, and other models utilized by the counterfeit identification system  106 . 
     The counterfeit identification system  106  also includes the storage manager  912 . The storage manager  912  stores data via one or more memory devices. In particular, the storage manager  912  stores the authentic digital images authentic digital images  914 , the digital images digital images  916 , and the authentic graphic features  918 . 
       FIGS.  1 - 9   , the corresponding text, and the examples provide a number of different methods, systems, devices, and non-transitory computer-readable media of the counterfeit identification system  106 . In addition to the foregoing, one or more embodiments can also be described in terms of flowcharts comprising acts for accomplishing the particular result, as shown in  FIG.  10   . The series of acts illustrated in  FIG.  10    may be performed with more or fewer acts. Further, the illustrated acts may be performed in different orders. Additionally, the acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar acts. 
       FIG.  10    illustrates a flowchart of a series of acts  1000  for determining that a digital image portrays a counterfeit product in accordance with one or more embodiments. In particular, the series of acts  1000  includes an act  1002  of extracting an authentic graphic element, an act  1004  of generating authentic graphic features, an act  1006  of extracting a graphic element, an act  1008  of generating graphic features, and an act  1010  of determining that the digital image portrays a counterfeit product. 
     As illustrated in  FIG.  10   , the series of acts  1000  includes the act  1002  of extracting an authentic graphic element. In particular, the act  1002  comprises extracting an authentic graphic element from an authentic digital image. In some embodiments, the act  1002  further comprises extracting an authentic graphic element from an authentic digital image by accessing a repository of authentic graphic elements. Additionally, in some embodiments, the act  1002  comprises extracting the graphic element by utilizing a machine learning model to generate a bounding box including a graphic element boundary, a mask region, and a confidence score indicating a likelihood the bounding box includes the graphic element. 
       FIG.  10    further includes the act  1004  of determining authentic graphic features. In particular, the act  1004  comprises determining authentic graphic features for the authentic graphic element, wherein the authentic graphic features comprise authentic shape features, authentic color features, and authentic text features. In some embodiments, the act  1004  includes determining the authentic graphic features by receiving authentic graphic feature ranges from a user client device associated with an authentic vendor. In some embodiments, the act  1004  also includes determining the authentic graphic features by determining authentic graphic features ranges. 
     The series of acts  1000  illustrated in  FIG.  10    also includes the act  1006  of extracting a graphic element. In particular, the act  1006  comprises extracting a graphic element from a digital image. In some embodiments, the act  1006  further comprises extracting a graphic element from a digital image by utilizing a machine learning model. 
     As further illustrated in  FIG.  10   , the series of acts  1000  includes the act  1008  of generating graphic features. In particular, the act  1008  comprises generating graphic features for the graphic element, wherein the graphic features comprise shape features, color features, and text features. Generally, the act  1008  can also include generating graphic features for the graphic element by: extracting shape features based on generating a normalized edge map of the graphic element; extracting color features based on mapping color values in a color space; and extracting text features by analyzing text properties. In some embodiments, the act  1008  further comprises extracting the graphic element by utilizing the machine learning model to generate a bounding box including a graphic element boundary, a mask region, and a confidence score indicating a likelihood the bounding box includes the graphic element. 
     In some embodiments, the act  1008  comprises generating the shape features for the graphic element by: generating an edge image utilizing a canny edge detector; and generating a shape representation indicating a collection of pixel coordinates corresponding to the edge image. In some embodiments, generating the edge image further comprises utilizing adaptive hysteresis. Furthermore, in one or more embodiments, the act  1008  comprises generating the color features by generating hue values and saturation values by mapping pixels from the graphic element into a color space. Additionally, in some embodiments, the act  1008  further comprises generating the text features by: identifying text within the graphic element by utilizing a text detection machine learning model; and extracting text properties comprising at least one of position, style, warp, and color. 
     Furthermore, in some embodiments, the act  1008  comprises generating the normalized edge map of the graphic element by: generating an edge image utilizing a canny edge detector and adaptive hysteresis; generating a shape representation indicating a collection of pixel coordinates corresponding to the edge image; and normalizing the collection of pixel coordinates. The act  1008  can further comprise extract the text features by: identifying text within the graphic element by utilizing a text detection machine learning model; and extracting the text properties comprising at least one of position, style, warp, and color. 
     The series of acts  1000  illustrated in  FIG.  10    includes the act  1010  of determining that the digital image portrays a counterfeit product. In particular, the act  1010  comprises determining that the digital image portrays a counterfeit product based on comparing the graphic features and the authentic graphic features. In some embodiments, the act  1010  comprises determining that the digital image portrays the counterfeit product by: utilizing a counterfeit detection model to generate similarity confidence scores based on analyzing the authentic graphic features and the graphic features; and determining that at least one of the similarity confidence scores falls below a threshold similarity value. In some embodiments, the similarity confidence scores comprise shape similarity scores, color similarity scores, and text similarity scores. 
     In addition (or in the alternative to) the acts described above, in some embodiments, the series of acts  1000  includes a step for determining that the digital image portrays a counterfeit product based on the authentic graphic features and the graphic features. For example, the acts described in reference to  FIGS.  7 A- 8 B  can comprise the corresponding acts (or structure) for performing a step for determining that the digital image portrays a counterfeit product based on the authentic graphic features and the graphic features. 
     Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein. 
     Computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the disclosure can comprise at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media. 
     Non-transitory computer-readable storage media (devices) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. 
     A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmissions media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media. 
     Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to non-transitory computer-readable storage media (devices) (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media (devices) at a computer system. Thus, it should be understood that non-transitory computer-readable storage media (devices) can be included in computer system components that also (or even primarily) utilize transmission media. 
     Computer-executable instructions comprise, for example, instructions and data which, when executed by a processor, cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed on a general-purpose computer to turn the general-purpose computer into a special purpose computer implementing elements of the disclosure. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims. 
     Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices. 
     Embodiments of the present disclosure can also be implemented in cloud computing environments. In this description, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing can be employed in the marketplace to offer ubiquitous and convenient on-demand access to the shared pool of configurable computing resources. The shared pool of configurable computing resources can be rapidly provisioned via virtualization and released with low management effort or service provider interaction, and then scaled accordingly. 
     A cloud-computing model can be composed of various characteristics such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth. A cloud-computing model can also expose various service models, such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth. In this description and in the claims, a “cloud-computing environment” is an environment in which cloud computing is employed. 
       FIG.  11    illustrates a block diagram of a computing device  1100  that may be configured to perform one or more of the processes described above. One will appreciate that one or more computing devices such as the computing device  1100  may implement the counterfeit identification system  106  and the online content management system  104 . As shown by  FIG.  11   , the computing device  1100  can comprise a processor  1102 , a memory  1104 , a storage device  1106 , an I/O interface  1108 , and a communication interface  1110 , which may be communicatively coupled by way of a communication infrastructure  1112 . In certain embodiments, the computing device  1100  can include fewer or more components than those shown in  FIG.  11   . Components of the computing device  1100  shown in  FIG.  11    will now be described in additional detail. 
     In one or more embodiments, the processor  1102  includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions for dynamically modifying workflows, the processor  1102  may retrieve (or fetch) the instructions from an internal register, an internal cache, the memory  1104 , or the storage device  1106  and decode and execute them. The memory  1104  may be a volatile or non-volatile memory used for storing data, metadata, and programs for execution by the processor(s). The storage device  1106  includes storage, such as a hard disk, flash disk drive, or other digital storage device, for storing data or instructions for performing the methods described herein. 
     The I/O interface  1108  allows a user to provide input to, receive output from, and otherwise transfer data to and receive data from computing device  1100 . The I/O interface  1108  may include a mouse, a keypad or a keyboard, a touch screen, a camera, an optical scanner, network interface, modem, other known I/O devices or a combination of such I/O interfaces. The I/O interface  1108  may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, the I/O interface  1108  is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation. 
     The communication interface  1110  can include hardware, software, or both. In any event, the communication interface  1110  can provide one or more interfaces for communication (such as, for example, packet-based communication) between the computing device  1100  and one or more other computing devices or networks. As an example, and not by way of limitation, the communication interface  1110  may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI. 
     Additionally, the communication interface  1110  may facilitate communications with various types of wired or wireless networks. The communication interface  1110  may also facilitate communications using various communication protocols. The communication infrastructure  1112  may also include hardware, software, or both that couples components of the computing device  1100  to each other. For example, the communication interface  1110  may use one or more networks and/or protocols to enable a plurality of computing devices connected by a particular infrastructure to communicate with each other to perform one or more aspects of the processes described herein. To illustrate, the digital content campaign management process can allow a plurality of devices (e.g., a client device and server devices) to exchange information using various communication networks and protocols for sharing information such as digital messages, user interaction information, engagement metrics, or campaign management resources. 
     In the foregoing specification, the present disclosure has been described with reference to specific exemplary embodiments thereof. Various embodiments and aspects of the present disclosure(s) are described with reference to details discussed herein, and the accompanying drawings illustrate the various embodiments. The description above and drawings are illustrative of the disclosure and are not to be construed as limiting the disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure. 
     The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. For example, the methods described herein may be performed with less or more steps/acts or the steps/acts may be performed in differing orders. Additionally, the steps/acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps/acts. The scope of the present application is, therefore, indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.