CN114819022A - Bar code encoding method, decoding method and equipment - Google Patents

Abstract

The application relates to a bar code encoding method, a bar code decoding method and bar code decoding equipment. The method comprises the following steps: acquiring an original bar code image and preset secret information; inputting the original bar code image and the preset secret information into a first neural network which is constructed in advance, so that the preset secret information and the original bar code image are combined in the up-sampling process of the first neural network; after the merging operation is finished, outputting a secret-carrying bar code image through the first neural network; and the secret-carrying bar code image is a bar code image containing the preset secret information. The scheme provided by the application can embed the preset secret information in the original barcode image, and realizes the expansion of the barcode capacity information.

Description

Bar code encoding method, decoding method and device

technical field

The present application relates to the technical field of barcodes, and in particular, to a barcode encoding method, decoding method and device.

Background technique

Barcodes (also known as barcodes) are unique worldwide identification cards that are machine-readable and are printable languages compared to machine-readable languages loaded on disks, tapes, and CDs. As one of the automatic identification technologies, barcode technology has made great progress in the past three decades, and barcode identification basically covers all products. Barcodes can be printed on packages and items, scanned by a digital camera or camera-equipped mobile phone, digitized, and information (such as product batch, production date, etc.) can be extracted. Barcodes have the characteristics of easy information management, and have been widely used in the field of product traceability.

However, current barcodes have limited capacity, making it difficult to embed some other traceability information. There is still no reliable solution to the problems of small amount of information and poor encryption in barcode technology.

SUMMARY OF THE INVENTION

In order to solve or partially solve the problems existing in the related art, the present application provides a barcode encoding method, decoding method and device, which can embed preset secret information on the original barcode image to realize the expansion of barcode capacity information.

A first aspect of the present application provides a barcode encoding method, including:

Obtain the original barcode image and preset secret information;

Inputting the original barcode image and the preset secret information into a pre-built first neural network, so that the preset secret information and the original barcode image are processed during the upsampling process of the first neural network. merge operation;

After the merging operation is completed, a password-carrying barcode image is output through the first neural network; wherein, the password-carrying barcode image is a barcode image containing the preset secret information.

In one embodiment, the merging operation of the preset secret information and the original barcode image includes:

converting the preset secret information into a target two-dimensional matrix;

A merge operation is performed on the target two-dimensional matrix and the feature map corresponding to the original barcode image.

In one embodiment, the converting the preset secret information into a target two-dimensional matrix includes:

converting the preset secret information into binary data;

converting the binary data into an initial two-dimensional matrix;

The initial two-dimensional matrix is up-sampled into a target two-dimensional matrix corresponding to the pixel size of the feature map.

In an embodiment, the first neural network is pre-built according to a first preset semantic segmentation network; wherein, the first preset semantic segmentation network includes a U-Net network.

In one embodiment, the password-carrying barcode image corresponds to pixels of the original barcode image; and/or,

Both the password-carrying barcode image and the original barcode image are one-dimensional barcode images.

A second aspect of the present application provides a barcode decoding method, including:

Obtain a password-carrying barcode image; wherein, the password-carrying barcode image is a barcode image containing preset secret information;

Inputting the password-carrying barcode image into a pre-built second neural network, so that the second neural network recognizes the image and obtains a first recognition result;

The preset secret information is output according to the first identification result.

In one embodiment, the second neural network recognizes the image to obtain a first recognition result, including:

The second neural network recognizes the image to obtain a pre-recognition result, and then performs spatial transformation on the pre-recognition result to obtain a first recognition result.

In an embodiment, the second neural network is pre-built according to a second preset semantic segmentation network and a preset spatial transformation network; wherein, the second preset semantic segmentation network includes a U-Net network.

A third aspect of the present application provides an electronic device, comprising:

processor; and

A memory having executable code stored thereon which, when executed by the processor, causes the processor to perform the method as described above.

A fourth aspect of the present application provides a computer-readable storage medium on which executable codes are stored, and when the executable codes are executed by a processor of an electronic device, the processor is caused to execute the above method.

The technical solution provided by this application can include the following beneficial effects:

In the method provided by this application, by acquiring the original barcode image and the preset secret information, the original barcode image and the preset secret information are input into the pre-built first neural network, so that the preset secret information is input during the upsampling process of the first neural network. The secret information is combined with the original barcode image to output the encrypted barcode image. In this way, the preset secret information can be embedded in the original barcode image, so as to realize the expansion of the barcode capacity information.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not limiting of the present application.

Description of drawings

The above and other objects, features and advantages of the present application will become more apparent from the more detailed description of the exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the exemplary embodiments of the present application. same parts.

1 is a schematic flowchart of a method for encoding a barcode according to an embodiment of the present application;

Fig. 2 is another schematic flowchart of the coding method of the barcode shown in the embodiment of the present application;

3 is a schematic diagram of a network structure of a U-Net network shown in an embodiment of the present application;

Fig. 4 is the original barcode image shown in the embodiment of the present application;

FIG. 5 is an image of a password-carrying barcode shown in an embodiment of the present application;

6 is a schematic flowchart of a barcode decoding method shown in an embodiment of the present application;

7 is another schematic flowchart of the barcode decoding method shown in the embodiment of the present application;

8 is a schematic diagram of an application scenario of a password-carrying barcode image shown in an embodiment of the present application;

9 is a schematic diagram of another application scenario of the password-carrying barcode image shown in the embodiment of the present application;

10 is a schematic diagram of a reading result of a password-carrying barcode image shown in an embodiment of the present application;

11 is a schematic structural diagram of a barcode encoding device shown in an embodiment of the present application;

12 is a schematic structural diagram of a barcode decoding apparatus shown in an embodiment of the present application;

13 is a schematic structural diagram of a barcode encoding and decoding system shown in an embodiment of the present application;

14 is a schematic diagram of an application scenario of the barcode encoding and decoding system shown in the embodiment of the present application;

15 is another schematic structural diagram of the barcode encoding and decoding system shown in the embodiment of the present application;

FIG. 16 is a schematic structural diagram of an electronic device shown in an embodiment of the present application.

Detailed ways

Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of this application to those skilled in the art.

The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application and the appended claims, the singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, such information should not be limited by these terms. These terms are only used to distinguish the same type of information from each other. For example, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may expressly or implicitly include one or more of that feature. In the description of the present application, "plurality" means two or more, unless otherwise expressly and specifically defined.

In the related art, the capacity of the barcode is limited, and it is difficult to embed some other traceability information. There is still no reliable solution to the problems of small amount of information and poor encryption in barcode technology.

In view of the above problems, the embodiments of the present application provide a barcode encoding method, which can embed preset secret information on the original barcode image, so as to realize the expansion of barcode capacity information.

The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.

FIG. 1 is a schematic flowchart of a barcode encoding method according to an embodiment of the present application. The method in the embodiment of FIG. 1 can be applied to an electronic device, and the electronic device can be an encoding device (or called an encoder).

Referring to Figure 1, the method includes:

Step S101 , acquiring the original barcode image and preset secret information.

Wherein, the original barcode image may be a one-dimensional barcode image. The original barcode image may be a commodity barcode (eg, EAN-13 code, EAN-128 code, etc.), and the barcode presented by the original barcode image follows the GB12904-2008 national standard. The original barcode image carries the basic information of the product (such as the country of production, manufacturer, product name, etc.), and the original barcode image is scanned and read by a common scanning reader in the market (such as a barcode scanner, a smartphone, etc.) Then you can get the basic information of its products.

The preset secret information may be commodity characteristic information such as the production batch and production date of the commodity. The preset secret information may be data in a text format.

Step S102: Input the original barcode image and the preset secret information into the pre-built first neural network, so that the preset secret information and the original barcode image are merged during the upsampling process of the first neural network.

The first neural network may be pre-built according to the first preset semantic segmentation network. Wherein, the first preset semantic segmentation network may be a U-Net network.

In this step, during the upsampling process of the first neural network, the preset secret information can be converted into a target two-dimensional matrix, and the target two-dimensional matrix and the feature map corresponding to the original barcode image can be merged. It can be understood that in this step, the preset secret information is fused with the original barcode image.

Step S103: After the merging operation is completed, output the password-carrying barcode image through the first neural network; wherein, the password-carrying barcode image is a barcode image containing preset secret information.

Wherein, both the encrypted barcode image and the original barcode image are one-dimensional barcode images, and the encrypted barcode image corresponds to the pixels of the original barcode image. That is to say, the cipher-carrying barcode image also carries the same basic commodity information as the original barcode image. After scanning and reading the cipher-carrying barcode image, the common scanning reader in the market can also obtain the basic commodity information carried in the original barcode image. information. The preset secret information carried in the password-carrying barcode image needs to be read by a dedicated scanning reader.

It can be seen from this embodiment that in the method provided by the embodiment of the present application, by acquiring the original barcode image and the preset secret information, the original barcode image and the preset secret information are input into the pre-built first neural network, so that in the first During the upsampling process of the neural network, the preset secret information is combined with the original barcode image to output the encrypted barcode image. In this way, the preset secret information can be embedded in the original barcode image, so as to realize the expansion of the barcode capacity information.

FIG. 2 is another schematic flowchart of a barcode encoding method according to an embodiment of the present application. FIG. 2 depicts the scheme of the present application in more detail relative to FIG. 1 .

Referring to Figure 2, the method includes:

Step S201 , acquiring the original barcode image and preset secret information.

For this step, reference may be made to the description in step S101, and details are not repeated here.

Step S202: Input the original barcode image and the preset secret information into the pre-built first neural network, so that the preset secret information is converted into a target two-dimensional matrix during the upsampling process of the first neural network, and the target two-dimensional The matrix is merged with the feature map corresponding to the original barcode image.

The first neural network may be pre-built according to the first preset semantic segmentation network. Wherein, the first preset semantic segmentation network may be a U-Net network. In the embodiment of the present application, the first neural network is pre-built according to the U-Net network. In one of the embodiments, the first neural network is obtained by adjusting and modifying the convolutional layer in the U-Net network.

The U-Net network is one of the algorithms for semantic segmentation using a fully convolutional network. Since the U-net network can perform pixel-level classification tasks, the output is the category of each pixel, and pixels of different categories will display different The color is often used in situations where the task is heavy and the picture data is relatively small. It can be seen that the barcode picture conforms to this situation, and the first neural network is pre-built according to the U-Net network, which can obtain two advantages including but not limited to the following: First, the output result can locate the position of the target category. The second is that data enhancement can be performed, which can solve the problem of less image data. The U-shaped structure of the U-Net network can make it use fewer training images, and at the same time, the segmentation accuracy is also higher.

Please refer to Figure 3. Figure 3 shows the network structure of the U-Net network. The overall process of the U-Net network is encoding (the left part of Figure 3) and decoding (the right part of Figure 3). Feature extraction is performed by downsampling during encoding; that is, the left-hand side of the U-Net network is a feature extraction network that uses conv and pooling. In the decoding process, the abstract features are restored to the size of the original image by upsampling, and finally the segmentation result is obtained; that is, the network on the right side of the U-Net network is a feature augmentation and fusion network, which uses upsampling to generate a feature map Cascade operation with concatenate, and then generate a feature map through a set number of convolutions (for example, twice), and use a set number of preset convolution kernels (for example, two convolution kernels with a size of 1*1). Classification.

Since the first neural network is pre-built according to the U-Net network, the first neural network belongs to a semantic segmentation network, and the first neural network can identify the original barcode in the original barcode image. In this application, the processing process of the first neural network can be understood as: first, extract features from the original barcode in the original barcode image, and then restore the extracted features to the feature map corresponding to the original barcode image in the upsampling process. When the feature map is merged with the target two-dimensional matrix converted by the preset secret information, and the convolution operation is performed for a set number of times (for example, twice), the output is obtained to obtain the final required password-carrying barcode image.

It can be understood that there may be multiple upsampling operations in the upsampling process of the first neural network. In the embodiment of the present application, the target two-dimensional matrix converted by the preset secret information may be corresponding to the original barcode image in any upsampling operation. The feature maps are merged.

In one embodiment, the preset secret information can be converted into a target two-dimensional matrix through preprocessing, including:

Step A: Convert the preset secret information into binary data.

In this step, the preset secret information in text format can be converted into binary data.

Step B: Convert the binary data to an initial 2D matrix.

Among them, the length and width of the initial two-dimensional matrix can be equal.

Step C: Upsampling the initial two-dimensional matrix into a target two-dimensional matrix corresponding to the pixel size of the feature map.

In this step, the initial two-dimensional matrix may be up-sampled into a target two-dimensional matrix corresponding to the pixel size of the feature map by interpolation.

In order to facilitate the understanding of the processing process of the first neural network, the following example is used to illustrate. For example, the pixels of the original barcode image are 400×400, and the feature map corresponding to the original barcode image generated during the upsampling process of the first neural network is 400 ×400; at the same time, the preset secret information of binary data can be converted into an initial two-dimensional matrix with pixels of 40×40, and then the initial two-dimensional matrix of 40×40 can be upsampled to a target of 400×400 by interpolation Two-dimensional matrix; it can be seen that the target two-dimensional matrix of 400×400 corresponds to the pixel size of the feature map of 400×400. By combining the target two-dimensional matrix of 400×400 and the feature map of 400×400, and then by After the convolution operation, the encrypted barcode image is output.

For another example, the pixels of the original barcode image are 400×400, and the feature map restored during the upsampling process of the first neural network is 200×200; at the same time, the preset secret information of binary data can be converted into pixels as The initial two-dimensional matrix of 40 × 40, and then the initial two-dimensional matrix of 40 × 40 is upsampled to the target two-dimensional matrix of 200 × 200 by interpolation; it can be seen that the target two-dimensional matrix of 200 × 200 and the features of 200 × 200 The pixel size of the image corresponds to the pixel size of the image. By combining the target two-dimensional matrix of 200×200 and the feature map of 200×200, the upsampling operation is performed again to the feature map of 400×400, and then the convolution operation is performed. Password barcode image.

Step S203: After the merging operation is completed, output the password-carrying barcode image through the first neural network; wherein, the password-carrying barcode image is a barcode image containing preset secret information.

It can be understood that, by performing a merging operation (for example, performing matrix addition), a password-carrying barcode image will be output after the first neural network convolution operation. In this embodiment of the present application, the password-carrying barcode image corresponds to the pixels of the original barcode image, for example, a password-carrying barcode image with pixels of 400×400 corresponds to an original barcode image with pixels of 400×400.

In this embodiment of the present application, preset secret information with a preset capacity may be obtained, and then a password-carrying barcode image may be generated according to the preset secret information with the preset capacity and the original barcode image, so as to expand the barcode capacity. For example, a password-carrying barcode image can be generated according to preset secret information with a capacity of 200 bits and a pre-obtained original barcode image, so that the generated secret-carrying barcode image is embedded with 200 bits of preset secret information.

Please refer to FIG. 4 and FIG. 5 together. FIG. 4 is an original barcode image shown in an embodiment of the present application, and FIG. 5 is a password-carrying barcode image shown in an embodiment of the present application. It can be found that the difference between the original barcode image and the encrypted barcode image is subtle, and it is difficult for the human eye to detect the difference. In other words, the human eye cannot clearly distinguish the difference between the original barcode image and the encrypted barcode image. In the process of generating the encrypted barcode image, the pre-built first neural network is used to realize the modulation and fusion of the pixels in the original barcode image, and the preset secret information is integrated into the original barcode image, so that the preset secret information is not easy to be found and obtained. , which ensures the encryption properties of the preset secret information in the encrypted barcode image.

It should be noted that, in the embodiment of the present application, both the password-carrying barcode image and the original barcode image are one-dimensional barcode images, and the password-carrying barcode image corresponds to the pixels of the original barcode image. That is to say, the cipher-carrying barcode image also carries the same basic commodity information as the original barcode image. After scanning and reading the cipher-carrying barcode image, the common scanning reader in the market can also obtain the basic commodity information carried in the original barcode image. information. The preset secret information carried in the password-carrying barcode image needs to be read by a dedicated scanning reader.

It can be seen from this embodiment that the method provided by the embodiment of the present application can embed preset secret information on the original barcode image, so as to realize the expansion of the barcode capacity information. It can be seen that the coding method of the barcode provided by this application, as a kind of steganography (steganography is a skill and science about information hiding, the so-called information hiding refers to not letting people other than the intended recipients Anyone who knows the transmission event of the information or the content of the information) can realize the high security hiding of the additional data information by the original barcode image, increase the existing capacity of the barcode, and can not affect the general scanning readers on the market (such as On the basis of identifying GS1 general specification barcodes, the expansion of additional information is realized. Secondly, especially in the logistics industry, a feasible solution is provided for realizing one code instead of multiple codes. By implementing the method of the present application, preset secret information can be embedded in the original barcode image on the commodity or item package, so that no need Paste additional barcodes on the product or item packaging again, which is beneficial to ensure the beautiful printing of the packaging and does not damage the spatial layout of the packaging surface. In addition, when accessing images and videos in a local area network, it may lead to leakage of privacy and even malicious tampering of content. With the method provided in this application, barcodes can be used as embedded carriers and used as anti-counterfeiting information for copyright protection of images and videos.

FIG. 6 is a schematic flowchart of a barcode decoding method according to an embodiment of the present application. The method in the embodiment of FIG. 1 may be applied to an electronic device, and the electronic device may be a decoding device (or called a decoder), and the decoding device may be a scanning reader, a smart phone, or the like.

Referring to Figure 6, the method includes:

Step S601 , acquiring an image of a password-carrying barcode; wherein, the password-carrying barcode image is a barcode image containing preset secret information.

The specific description of the password-carrying barcode image may refer to the embodiment in FIG. 1 or FIG. 2 , which will not be repeated here.

Step S602: Input the password-carrying barcode image into a pre-built second neural network, so that the second neural network recognizes the image and obtains a first recognition result.

In one embodiment, the second neural network is pre-built according to a second preset semantic segmentation network; wherein the second preset semantic segmentation network includes a U-Net network. That is to say, the second neural network is a semantic segmentation network, and the second neural network identifies image data corresponding to preset secret information according to the encrypted barcode image, and the image data is the obtained first identification result.

In another embodiment, the second neural network is pre-built according to the second preset semantic segmentation network and the preset spatial transformation network; wherein the second preset semantic segmentation network includes a U-Net network. In this embodiment, recognizing the image by the second neural network to obtain the first recognition result includes: recognizing the image by the second neural network to obtain the pre-recognition result, and then performing spatial transformation on the pre-recognition result to obtain the first recognition result. That is to say, the second neural network is used as a semantic segmentation network, and the second neural network identifies the image data corresponding to the preset secret information according to the encrypted barcode image. The image data containing the recognition result is subjected to spatial transformation, and the image data after the spatial transformation is completed is the obtained first recognition result.

Step S603, outputting preset secret information according to the first identification result.

In this step, according to the first identification result, the expressed information can be obtained according to a preset algorithm, that is, preset secret information can be obtained.

It can be seen from this embodiment that the method provided by the embodiment of the present application can decode the encrypted barcode image and obtain the preset secret information in the encrypted barcode image.

FIG. 7 is another schematic flowchart of a barcode decoding method according to an embodiment of the present application. FIG. 7 describes the scheme of the present application in more detail relative to FIG. 6 .

Referring to Figure 7, the method includes:

Step S701 , acquiring a password-carrying barcode image; wherein, the password-carrying barcode image is a barcode image containing preset secret information.

The specific description of the password-carrying barcode image may refer to the embodiment in FIG. 1 or FIG. 2 , which will not be repeated here.

It should be noted that the password-carrying barcode image may be an electronic image, that is, the password-carrying barcode image is displayed on an electronic display device (eg, a computer screen). The password-carrying barcode image can be a printed image, that is to say, the password-carrying barcode image can be a printed paper image, for example, it can be a printed image of the password-carrying barcode image existing in the form of an electronic image in a computer. For example, it may be a printed image that is printed out after photographing a password-carrying barcode image by a photographing device.

In this embodiment of the present application, the password-carrying barcode image can be acquired by scanning. For example, scanning with a scanning reader to obtain a password-bearing barcode image.

Step S702, inputting the password-carrying barcode image into a pre-built second neural network, so that the second neural network recognizes the image to obtain a pre-recognition result, and then performs spatial transformation on the pre-recognition result to obtain a first recognition result.

In this embodiment, the second neural network is pre-built according to the second preset semantic segmentation network and the preset spatial transformation network; wherein the second preset semantic segmentation network includes a U-Net network. In this step, the second neural network is used as a semantic segmentation network, and the second neural network recognizes the image data corresponding to the preset secret information according to the encrypted barcode image, and the image data is the pre-recognition result, which is then used as The image data of the pre-recognition result is spatially transformed, and the image data after the spatial transformation is completed is the obtained first identification result.

It can be understood that the image data (ie, feature map, feature map) that is the result of the pre-recognition is subjected to spatial transformation, the purpose of which is to correct the image data, adjust the posture of the image data, and use the image data after the spatial transformation is completed as the first identification. As a result, it is convenient to obtain the preset secret information from the first identification result subsequently, which is beneficial to improve the accuracy of obtaining the preset secret information.

It should be noted that the second neural network is a trained network for recovering the hidden preset secret information from the encrypted barcode image. Compared with the first neural network in the embodiment of FIG. 1 or FIG. 2, the second neural network can also be pre-built according to the U-Net network, and the second neural network can be regarded as the inverse transformation of the first neural network. . In the second neural network, in order to enhance the robustness, a preset spatial transformation network (such as SpatialTransformer Network, referred to as STN network) is introduced, which provides a corresponding spatial transformation method for the input in the network structure, and the transformation method includes but Not limited to scaling, shearing and rotation, so that the second neural network can learn the characteristics of space invariance. The encrypted barcode image is processed through a series of convolutions, dense layers and sigmoid functions to produce a final output of the same length as the embedded preset secret information to recover the preset secret information. In this embodiment of the present application, a cross-entropy loss function may be used to supervise the second neural network.

Since the second neural network can be pre-built according to the U-Net network and the preset space transformation network, the second neural network also belongs to a semantic segmentation network, and the network structure of the second neural network includes the U-Net network and the space transformation network. The network structure, the output of the U-Net network in the second neural network is the input of the preset spatial transformation network, and the output of the preset spatial transformation network is used as the first recognition result. The convolutional layer of the U-Net network in the second neural network can be adjusted and modified as required.

The password-carrying barcode image is a barcode image containing preset secret information. In other words, the original barcode image becomes an encrypted barcode image after the preset secret information is embedded. In the embodiment of the present application, the second neural network is used as a semantic segmentation network. The second neural network can not only identify the image data corresponding to the preset secret information from the encrypted barcode image, but also identify the image data from the encrypted barcode image. The image data corresponding to the original barcode image is output. In this embodiment of the present application, the second neural network only selects image data (ie, the first recognition result) corresponding to the preset secret information for output.

Step S703, outputting preset secret information according to the first identification result.

In this step, according to the first identification result, the expressed information can be obtained according to a preset algorithm, that is, preset secret information can be obtained. In one embodiment, the first recognition result may correspond to a matrix data, and the matrix data is converted into preset secret information in a text format.

It should be noted that both the encrypted barcode image and the original barcode image are one-dimensional barcode images, and the encrypted barcode image corresponds to the pixels of the original barcode image. That is to say, the password-carrying barcode image also carries the same basic commodity information as the original barcode image. After scanning and reading the password-carrying barcode image, the general scanning reader on the market can also obtain the basic commodity information carried in the original barcode image. information. The preset secret information carried in the password-carrying barcode image needs to be read by a dedicated scanning reader. In this embodiment of the present application, the preset secret information carried in the password-carrying barcode image can be obtained by reading a decoding device that executes the method in the embodiment shown in FIG. 6 or FIG. 7 , and the decoding device may be a scanning Reader (ie scan code gun).

It can be understood that the image of the password-carrying barcode may be an electronic image or a printed image. Please refer to FIG. 8 , the embodiment of FIG. 8 shows a schematic diagram of a scenario in which a password-carrying barcode image is scanned and read by a scanning reader commonly used in the market. In the embodiment of FIG. 8 , the image of the password-carrying barcode is presented in the form of an electronic image in an electronic display device (ie, a computer screen), and a general-purpose scanner reader on the market can successfully scan and read the image of the password-carrying barcode presented in the form of an electronic image. , so as to obtain the basic information of the commodity carried in the original barcode image (the data shown in the left area in Figure 8).

Please refer to FIG. 9 , the embodiment of FIG. 9 shows a schematic diagram of another scenario in which a password-carrying barcode image is scanned and read by a scanning reader commonly used in the market. In the embodiment of FIG. 9, the image of the password-carrying barcode is presented in the form of a printed image, and the scanning reader commonly used in the market can successfully scan and read the image of the password-carrying barcode presented in the form of a printed image, thereby obtaining the original barcode image carried in the image. Basic commodity information (data shown in the left area in Figure 9).

Please refer to FIG. 10 . The embodiment of FIG. 10 shows a schematic diagram of the result of scanning and reading a password-carrying barcode image by a decoding device. After the encrypted barcode image is scanned and read by the decoding device, preset secret information (the data at the upper left of the barcode as shown in FIG. 10 ) can be obtained. The decoding device executes the method in the embodiment shown in FIG. 6 or FIG. 7 , and the decoding device may be a scanning reader (ie, a scanning code gun).

It can be seen from this embodiment that the method provided by the embodiment of the present application can decode the password-carrying barcode image by using the pre-built second neural network to obtain preset secret information in the password-carrying barcode image.

Corresponding to the foregoing application function implementation method embodiments, the present application further provides an embodiment of a barcode encoding apparatus.

FIG. 11 is a schematic structural diagram of an apparatus for encoding a barcode according to an embodiment of the present application.

Referring to FIG. 11 , a barcode encoding apparatus 1100 includes: a first acquisition module 1110 , an encoding module 1120 , and a first output module 1130 .

The first acquisition module 1110 is used to acquire the original barcode image and preset secret information.

The encoding module 1120 is configured to input the original barcode image and the preset secret information into the pre-built first neural network, so that the preset secret information and the original barcode image are merged during the upsampling process of the first neural network.

The first output module 1130 is configured to output the password-carrying barcode image through the first neural network after the merging operation is completed; wherein, the password-carrying barcode image is a barcode image containing preset secret information.

It can be seen from this embodiment that the barcode encoding apparatus 1100 provided by the present application can embed preset secret information on the original barcode image, so as to realize the expansion of barcode capacity information.

FIG. 12 is a schematic structural diagram of a barcode decoding apparatus shown in an embodiment of the present application.

Referring to FIG. 12 , a barcode decoding apparatus 1200 includes: a second acquisition module 1210 , a decoding module 1220 , and a second output module 1230 .

The second acquiring module 1210 is configured to acquire a password-carrying barcode image, wherein the password-carrying barcode image is a barcode image containing preset secret information.

The decoding module 1220 is configured to input the encrypted barcode image into the pre-built second neural network, so that the second neural network recognizes the image and obtains the first recognition result.

The second output module 1230 is configured to output preset secret information according to the first identification result.

It can be seen from this embodiment that the barcode decoding apparatus 1200 provided by the present application can decode the encrypted barcode image and obtain the preset secret information in the encrypted barcode image.

Regarding the apparatus in the above-mentioned embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be described in detail here.

FIG. 13 is a schematic structural diagram of a barcode encoding and decoding system according to an embodiment of the present application.

Referring to FIG. 13 , a barcode encoding and decoding system 1300 includes: an encoding device 1310 and a decoding device 1320 .

The encoding device is used to obtain the original barcode image and the preset secret information; the original barcode image and the preset secret information are input into the pre-built first neural network, so that the preset secret information is stored in the up-sampling process of the first neural network. Perform a merging operation with the original barcode image; after the merging operation is completed, output the encrypted barcode image through the first neural network; wherein, the encrypted barcode image is a barcode image containing preset secret information.

The decoding device is used to obtain the encrypted barcode image; wherein, the encrypted barcode image is a barcode image containing preset secret information; input the encrypted barcode image into a pre-built second neural network, so that the second neural network recognizes the image, Obtain a first identification result; output preset secret information according to the first identification result.

It can be seen from this embodiment that the barcode encoding and decoding system 1300 provided by the present application can embed preset secret information on the original barcode image, obtain a barcode image carrying a password, realize the expansion of the barcode capacity information, and can The barcode image is decoded to obtain the preset secret information in the encrypted barcode image.

Please refer to FIG. 14. FIG. 14 is a schematic diagram of an application scenario of the barcode encoding and decoding system shown in the embodiment of the present application. The original barcode image in the lower left corner of Figure 14 and the preset secret information (the steganographic information shown in the upper left corner of Figure 14, that is, the website information) can be encoded by an encoding device, so as to obtain an encrypted barcode image, and the encrypted barcode image is encoded by After encoding by the device (such as the smart phone in FIG. 14 ), the preset secret information in the password-carrying barcode image (the website information shown in the upper right corner of FIG. 14 ) can be obtained.

Further, please refer to FIG. 15. FIG. 15 is another schematic structural diagram of the barcode encoding and decoding system shown in the embodiment of the present application. The barcode encoding and decoding system further includes: a prediction device 1330 . The prediction device 1330 is used to detect the location area of the encrypted barcode image from the electronic image or the printed image, so that the decoding device 1320 can decode the encrypted barcode image to identify preset secret information in the encrypted barcode image.

As shown in FIG. 15 , in one of the embodiments, after the encoding device 1310 encodes the original barcode image and the preset secret information to generate an encrypted barcode image, the decoding device 1320 can decode the encrypted barcode image to obtain a pre-coded barcode image. Set up secret information. In another embodiment, since the encrypted barcode image can be presented in the form of an electronic image or a printed image, in order to facilitate the decoding device 1320 to decode and identify the preset secret information from the electronic image or the printed image, the electronic image or the printed image is first After processing by the prediction device 1330, after the prediction device 1330 detects the location area occupied by the password-carrying barcode image in the electronic image or the printed image, the decoding device 1320 is used for decoding, so as to improve the decoding success rate and reduce the decoding difficulty.

FIG. 16 is a schematic structural diagram of an electronic device shown in an embodiment of the present application. The electronic device can be an encoding device or a decoding device. In other words, when the method of the embodiment shown in FIG. 1 or FIG. 2 is executed, the electronic device is an encoding device; when the method of the embodiment shown in FIG. 6 or FIG. 7 is executed, the electronic device is an encoding device. The electronic device is a decoding device.

Referring to FIG. 16 , an electronic device 1600 includes a memory 1610 and a processor 1620 .

The processor 1620 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field-available processor Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Memory 1610 may include various types of storage units, such as system memory, read only memory (ROM), and persistent storage. The ROM may store static data or instructions required by the processor 1620 or other modules of the computer. Persistent storage devices may be readable and writable storage devices. Permanent storage may be a non-volatile storage device that does not lose stored instructions and data even if the computer is powered off. In some embodiments, persistent storage devices employ mass storage devices (eg, magnetic or optical disks, flash memory) as persistent storage devices. In other embodiments, persistent storage may be a removable storage device (eg, a floppy disk, an optical drive). System memory can be a readable and writable storage device or a volatile readable and writable storage device, such as dynamic random access memory. System memory can store some or all of the instructions and data that the processor needs at runtime. Additionally, memory 1610 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (eg, DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and magnetic and/or optical disks may also be employed. In some embodiments, memory 1610 may include a removable storage device that is readable and/or writable, such as a compact disc (CD), a read-only digital versatile disc (eg, DVD-ROM, dual-layer DVD-ROM), Read-only Blu-ray Disc, Ultra-Density Disc, Flash Card (eg SD Card, Min SD Card, Micro-SD Card, etc.), Magnetic Floppy Disk, etc. Computer-readable storage media do not contain carrier waves and transient electronic signals transmitted over wireless or wireline.

Executable codes are stored on the memory 1610, and when the executable codes are processed by the processor 1620, the processor 1620 can be caused to execute some or all of the above-mentioned methods.

Furthermore, the method according to the present application can also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps in the above method of the present application.

Alternatively, the present application can also be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) on which executable codes (or computer programs or computer instruction codes) are stored, When the executable code (or computer program or computer instruction code) is executed by the processor of the electronic device (or server, etc.), the processor is caused to perform some or all of the steps of the above-mentioned method according to the present application.

Various embodiments of the present application have been described above, and the foregoing descriptions are exemplary, not exhaustive, and not limiting of the disclosed embodiments. Numerous modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or improvement over the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. A method of encoding a barcode, comprising:

acquiring an original bar code image and preset secret information;

inputting the original bar code image and the preset secret information into a first neural network which is constructed in advance, so that the preset secret information and the original bar code image are combined in the up-sampling process of the first neural network;

after the merging operation is finished, outputting a secret-carrying bar code image through the first neural network; and the secret-carrying bar code image is a bar code image containing the preset secret information.

2. The method of claim 1, wherein the merging the predetermined covert information with the original barcode image comprises:

converting the preset secret information into a target two-dimensional matrix;

and merging the target two-dimensional matrix and the characteristic graph corresponding to the original bar code image.

3. The method according to claim 2, wherein the converting the predetermined stego information into a target two-dimensional matrix comprises:

converting the preset secret information into binary data;

converting the binary data into an initial two-dimensional matrix;

and upsampling the initial two-dimensional matrix into a target two-dimensional matrix corresponding to the pixel size of the characteristic diagram.

4. The method of claim 1, wherein:

the first neural network is obtained by pre-constructing according to a first preset semantic segmentation network; the first preset semantic segmentation network comprises a U-Net network.

5. The method of claim 1, wherein:

the secret-carrying bar code image corresponds to the original bar code image in pixel; and/or the presence of a gas in the gas,

the secret-carrying bar code image and the original bar code image are both one-dimensional bar code images.

6. A method of decoding a barcode, comprising:

acquiring a secret-carrying bar code image; the secret-carrying bar code image is a bar code image containing preset secret information;

inputting the secret bar code image into a pre-constructed second neural network so that the second neural network identifies the image to obtain a first identification result;

and outputting the preset secret information according to the first identification result.

7. The method of claim 6, wherein the second neural network identifies the image, resulting in a first identification result, comprising:

and the second neural network identifies the image to obtain a pre-identification result, and then performs spatial transformation on the pre-identification result to obtain a first identification result.

8. The method of claim 6, wherein:

the second neural network is obtained by pre-constructing according to a second preset semantic segmentation network and a preset spatial transformation network; and the second preset semantic segmentation network comprises a U-Net network.

9. An electronic device, comprising:

a processor; and

a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any one of claims 1-8.

10. A computer-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform the method of any one of claims 1-8.

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