Luxury authentic identification method and system based on single classification

Abstract

The invention provides a luxury authentic identification method and system based on single classification, and the method comprises the steps: obtaining an original leather label sample image, and carrying out the image enhancement preprocessing of the original leather label sample image; inputting the preprocessed original leather label sample image into a pre-trained independent authentic identification point segmentation network, wherein the independent authentic identification point segmentation network outputs a segmented independent authentic identification point segmentation image; inputting the independent authentic identification point segmented image into a single classification model pre-trained by adopting training data only comprising a forward label, wherein the single classification model outputs a positive sample reconstructed image; and calculating a difference value between the independent authentic identification point segmentation image and the positive sample reconstruction image, and determining an authentic identification result based on the difference value. According to the scheme, only the training data of the forward label can be adopted for training, and a single classification mode is adopted, so that the problem of precision reduction caused by sample imbalance of the luxury data set is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of identification and authentication, and in particular to a method and system for authenticating luxury goods based on a single classification. Background Art

[0002] In today's society, luxury goods are not only a synonym for quality and craftsmanship, but also a symbol of status and a reflection of life attitude.

[0003] First, from the perspective of consumer rights protection, genuine luxury goods often represent excellent quality and perfect after-sales service. Counterfeit and shoddy products are not only difficult to guarantee in quality, but may also pose safety hazards, seriously threatening consumers' health and user experience. Secondly, the healthy and orderly development of the luxury goods market is inseparable from strict authenticity identification. The proliferation of counterfeit luxury goods will disrupt the market order, damage the interests of regular brands and dealers, and further affect the innovation and vitality of the entire industry.

[0004] However, in the process of constructing luxury data sets, there are often situations where there is only a single type of data. There are often many reasons for this situation. For example, there are many manufacturers of counterfeit products, and the differences between them are reflected in various places. For example, the level and craftsmanship of counterfeiting are uneven, the materials used, and the counterfeit equipment series will all lead to various differences in counterfeits. The ultimate consequence of these differences is that the possibility of counterfeit series is endless, which makes it impossible to collect all the samples. In the current field of luxury goods authentication, the existing technology regards the authenticity identification of luxury goods as a classification problem to be solved. Therefore, it is limited by factors such as the imbalance and small number of samples in the luxury goods data set, resulting in limited generalization performance and accuracy of the model. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a luxury goods authentication method based on a single classification to eliminate or improve one or more defects existing in the prior art.

[0006] One aspect of the present invention provides a method for identifying counterfeit luxury goods based on a single classification, the method comprising the following steps:

[0007] Acquire an original leather swab sample image, and perform image enhancement preprocessing on the original leather swab sample image;

[0008] Inputting the preprocessed original leather tag sample image into a pre-trained independent counterfeit detection point segmentation network, the independent counterfeit detection point segmentation network outputs a segmented independent counterfeit detection point segmentation image;

[0009] Inputting the independent counterfeit detection point segmented image into a single classification model pre-trained with training data including only positive labels, the single classification model outputting a positive sample reconstructed image;

[0010] The difference value between the independent detection point segmented image and the positive sample reconstructed image is calculated, and the detection result is determined based on the difference value.

[0011] By adopting the above scheme, the single classification model of this scheme is trained with training data that only includes positive labels. This scheme can make the reconstructed image of the positive sample approach the image of the true mark. This scheme further calculates the difference value between the independent anti-counterfeiting point segmentation image and the reconstructed image of the positive sample. The greater the difference between the two, the greater the difference brought about by the processing of the single classification model, which means that the probability of false is greater. In summary, this scheme can be trained with only training data with positive labels, and adopts a single classification method to solve the problem of decreased accuracy caused by the imbalance of samples in the luxury data set.

[0012] In some embodiments of the present invention, the step of performing image enhancement preprocessing on the original leather swab sample image includes performing image enhancement using a preset image enhancement algorithm, wherein the image enhancement algorithm includes gamma correction, an adaptive contrast enhancement algorithm, and background noise filtering.

[0013] In some embodiments of the present invention, the method further comprises:

[0014] Using Hough transform to detect straight lines in the original leather tag sample image;

[0015] The bevel angle of each straight line is calculated, and the data dispersion is calculated using the interquartile range method to determine whether the original leather swab sample image is qualified.

[0016] In some embodiments of the present invention, the independent authentication point segmentation network is a YOLO v7 network.

[0017] In some embodiments of the present invention, in the step of inputting the independent counterfeit detection point segmentation image into a single classification model that is pre-trained using training data that only includes positive labels, and the single classification model outputs a reconstructed image of positive samples, the single classification model includes a fine-grained encoder and a fine-grained decoder, and the independent counterfeit detection point segmentation image is sequentially processed by the fine-grained encoder and the fine-grained decoder, and the reconstructed image of the positive samples is output.

[0018] In some embodiments of the present invention, the fine-grained encoder includes a first encoding convolution layer, a first encoding activation function layer, a second encoding convolution layer, a first encoding normalization layer, a third encoding convolution layer, a second encoding normalization layer, a fourth encoding convolution layer, a second encoding activation function layer, a third encoding normalization layer, a first encoding pooling layer, a hyperbolic tangent activation function layer and a Flatten layer connected sequentially.

[0019] In some embodiments of the present invention, in the step of sequentially processing the independent false detection point segmentation image through a fine-grained encoder and a fine-grained decoder and outputting a reconstructed image of a positive sample, the independent false detection point segmentation image is input into the first encoding convolutional layer of the fine-grained encoder, and the latent vector is output through the Flatten layer.

[0020] In some embodiments of the present invention, the fine-grained decoder includes a sequentially connected zeroth decoding convolution layer, a zeroth decoding normalization layer, a first decoding convolution layer, a first decoding normalization layer, a second decoding convolution layer, a second decoding normalization layer, a third decoding convolution layer, a third decoding normalization layer, a Dropout layer, a fourth decoding convolution layer and a hyperbolic tangent activation function layer.

[0021] In some embodiments of the present invention, in the step of sequentially processing the independent detection point segmentation image through a fine-grained encoder and a fine-grained decoder and outputting a reconstructed image of the positive sample, the latent vector is input into the zeroth decoding convolution layer of the fine-grained decoder, and the positive sample reconstructed image is output through the hyperbolic tangent activation function layer of the fine-grained decoder.

[0022] In some embodiments of the present invention, in the step of calculating the difference value between the independent detection point segmented image and the reconstructed image of the positive sample and determining the detection result based on the difference value, the structural similarity index of the independent detection point segmented image and the reconstructed image of the positive sample is calculated, and the calculated structural similarity index is compared with a preset threshold to determine the detection result.

[0023] The second aspect of the present invention also provides a luxury goods authentication system based on a single classification, the system comprising a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0024] The third aspect of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps implemented by the aforementioned luxury goods authentication method based on a single classification.

[0025] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention can be specifically pointed out and obtained in the specification and the accompanying drawings.

[0026] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention.

[0028] Figure 1 A schematic diagram of an implementation of a luxury goods authentication method based on a single classification according to the present invention;

[0029] Figure 2 Schematic diagram of the processing architecture of the training process of the luxury goods authentication method based on single classification according to the present invention;

[0030] Figure 3 is a schematic diagram of the architecture of a single classification model of the present invention;

[0031] Figure 4 It is a schematic diagram of the structure of a fine-grained encoder of a single classification model of the present invention;

[0032] Figure 5 Schematic diagram of the structure of the fine-grained decoder of the single classification model of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0034] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0035] like Figure 1 As shown, the present invention proposes a method for identifying counterfeit luxury goods based on a single classification, the method comprising the following steps:

[0036] Step S100, obtaining an original leather swab sample image, and performing image enhancement preprocessing on the original leather swab sample image;

[0037] In the specific implementation process, in the step of image enhancement preprocessing of the original leather tag sample image, this solution uses a variety of image enhancement algorithms for processing, such as gamma correction, adaptive contrast enhancement algorithm, and background noise filtering. This part mainly processes the texture of the image. The background noise is suppressed through the enhancement algorithm, and the required core authentication area is highlighted.

[0038] Step S200, inputting the preprocessed original leather tag sample image into a pre-trained independent counterfeit detection point segmentation network, and the independent counterfeit detection point segmentation network outputs a segmented independent counterfeit detection point segmentation image;

[0039] In a specific implementation process, the independent authentication point segmentation network is a YOLO v7 network.

[0040] In the specific implementation process, for a whole leather tag, not all the authentication points on it have the same differences. For example, the letter R in PARIS is quite different between the authentic and the counterfeit. At the same time, if a whole leather tag is sent to the network for learning, it needs to process too much content, which will cause the model's attention to be dispersed and difficult to focus on the parts with large differences. Therefore, this scheme uses an independent authentication point segmentation network to segment the independent object authentication points with key differences. This is also a hard attention mechanism, which guides the model's attention to more subtle core authentication differences.

[0041] Step S300, inputting the independent counterfeit detection point segmentation image into a single classification model pre-trained with training data including only positive labels, and the single classification model outputs a positive sample reconstructed image;

[0042] Step S400, calculating the difference between the independent detection point segmented image and the positive sample reconstructed image, and determining the detection result based on the difference.

[0043] In the specific implementation process, the existing luxury goods authentication technology treats the authenticity identification of luxury goods as a classification problem, and is therefore constrained by the imbalance and small number of authentic and counterfeit samples, resulting in limited generalization, performance, and accuracy of the model. This solution proposes a single classification algorithm, which treats the authentication of luxury goods as a single classification problem, greatly alleviating the requirements for sample collection and having high application capabilities.

[0044] By adopting the above scheme, the single classification model of this scheme is trained with training data that only includes positive labels. This scheme can make the reconstructed image of the positive sample approach the image of the true mark. This scheme further calculates the difference value between the independent anti-counterfeiting point segmentation image and the reconstructed image of the positive sample. The greater the difference between the two, the greater the difference brought about by the processing of the single classification model, which means that the probability of false is greater. In summary, this scheme can be trained with only training data with positive labels, and adopts a single classification method to solve the problem of decreased accuracy caused by the imbalance of samples in the luxury data set.

[0045] In some embodiments of the present invention, the step of performing image enhancement preprocessing on the original leather swab sample image includes performing image enhancement using a preset image enhancement algorithm, wherein the image enhancement algorithm includes gamma correction, an adaptive contrast enhancement algorithm, and background noise filtering.

[0046] In the specific implementation process, gamma correction edits the gamma curve of the image to detect the dark and light parts in the image signal and increase the ratio of the two, thereby improving the image contrast effect; the basic idea of ​​the adaptive contrast enhancement algorithm is to divide the image into two parts, low-frequency and high-frequency, for processing. The low-frequency part mainly reflects the background information of the image, while the high-frequency part contains the details and edge information of the image. By enhancing the high-frequency part, the contrast of the image can be effectively improved, making the details of the image clearer.

[0047] In some embodiments of the present invention, the method further comprises:

[0048] Using Hough transform to detect straight lines in the original leather tag sample image;

[0049] The bevel angle of each straight line is calculated, and the data dispersion is calculated using the interquartile range method to determine whether the original leather swab sample image is qualified.

[0050] In some embodiments of the present invention, the independent authentication point segmentation network is a YOLO v7 network.

[0051] like Figure 3 , 4 As shown in Figure 5, in some embodiments of the present invention, in the step of inputting the independent counterfeit detection point segmentation image into a single classification model that is pre-trained using training data that only includes positive labels, and the single classification model outputs a reconstructed image of positive samples, the single classification model includes a fine-grained encoder and a fine-grained decoder, and the independent counterfeit detection point segmentation image is sequentially processed by the fine-grained encoder and the fine-grained decoder, and the positive sample reconstructed image is output.

[0052] In some embodiments of the present invention, the fine-grained encoder includes a first encoding convolution layer, a first encoding activation function layer, a second encoding convolution layer, a first encoding normalization layer, a third encoding convolution layer, a second encoding normalization layer, a fourth encoding convolution layer, a second encoding activation function layer, a third encoding normalization layer, a first encoding pooling layer, a hyperbolic tangent activation function layer and a Flatten layer connected sequentially.

[0053] In some embodiments of the present invention, in the step of sequentially processing the independent false detection point segmentation image through a fine-grained encoder and a fine-grained decoder and outputting a reconstructed image of a positive sample, the independent false detection point segmentation image is input into the first encoding convolutional layer of the fine-grained encoder, and the latent vector is output through the Flatten layer.

[0054] In some embodiments of the present invention, the fine-grained decoder includes a sequentially connected zeroth decoding convolution layer, a zeroth decoding normalization layer, a first decoding convolution layer, a first decoding normalization layer, a second decoding convolution layer, a second decoding normalization layer, a third decoding convolution layer, a third decoding normalization layer, a Dropout layer, a fourth decoding convolution layer and a hyperbolic tangent activation function layer.

[0055] In some embodiments of the present invention, in the step of sequentially processing the independent detection point segmentation image through a fine-grained encoder and a fine-grained decoder and outputting a reconstructed image of the positive sample, the latent vector is input into the zeroth decoding convolution layer of the fine-grained decoder, and the positive sample reconstructed image is output through the hyperbolic tangent activation function layer of the fine-grained decoder.

[0056] Single classification network construction and algorithm flow: First, the codec at the core of the network. After our comparison, we finally adopted the codec structure of DCGAN. This is because our algorithm does not require a particularly complex codec, but we added a variable image size processing module consisting of a global average pooling and a batch normalization layer to DCGAN. This is because the original OCGAN can only run on a set of handwritten digits, and its resolution is 18*18, while our image resolution is 448*448. We can perform pooling based on images of different resolutions to achieve the same processing size. At the same time, since we will use randomly distributed vectors in the algorithm, we also proposed an image interpolation module. When the size of the processed image is smaller than the randomly distributed vector, we will use the interpolation algorithm to fill in the missing part, so as to ensure that the two tensors will not go wrong during processing.

[0057] The single classification model of this scheme adopts a single classification algorithm. The core of the single classification algorithm is a reconstruction-based algorithm. Since only the positive class is learned, the model will reconstruct all images into positive samples, and then judge them through the reconstruction error. This requires the model to be able to achieve the following two points: first, it can reconstruct the positive samples very well, and second, it can reconstruct the negative samples into positive samples very well. Only then will the reconstruction error be large. Therefore, it is necessary to limit the sample space of the model to the positive sample space, rather than diverging to all categories.

[0058] like Figure 2 and 3 As shown, the single classification algorithm also includes a visual discriminator Dv, a latent space discriminator Dl, a classifier C, and the encoder En and decoder De mentioned above. The parameter update of each module complies with the following rules:

[0059] All parameters except the classifier C are fixed, n is random noise, the input image is x, l1 is En(x+n) which represents the encoder's processing of the noise input, l2 is a random distribution vector satisfying (-1,1), and the updated classifier C is: C(De(l2),0)+C(De(l1),1), which means that the classifier is updated to the positive class 1 after decoding the input image, and the random distribution is updated to the negative class 0.

[0060] Update the latent space discriminator Dl: Dl(l1,0)+Dl(l2,1). At the latent space level, the closer it is to random distribution, the update is towards the positive class 1, and the closer it is to the input, the update is towards the negative class 0. The purpose is to limit the recognition ability of the model; update the visual discriminator Dv: Dv(De(l2),0)+Dv(x,1), which means that the closer it is to the real input image, the update is towards the positive class 1, and the closer it is to random distribution, the update is towards the negative class 0, so as to ensure the visual discriminator's discrimination of real images.

[0061] Negative information mining, updating the random distribution of l2, through C(De(l2),1), that is, letting the classifier look for randomly distributed samples that can be identified as positive class 1, that is, finding some negative information to increase the training difficulty of the model and improve the robustness of the model.

[0062] Update the encoder and decoder, and the loss function is constructed as Dl loss + Dv loss + λMse loss. Specifically, it is expressed as:

[0063] loss_ae_all=10.0*loss_ssim+loss_ae_v+loss_ae_l

[0064] loss_ae_all represents the total loss, 10.0*loss_ssim represents λMse loss, loss_ae_v represents Dl loss, and loss_ae_l represents Dl loss.

[0065] The above scheme is adopted, where loss_ae_v is the visual space discriminator loss, that is, the loss after the visual space discriminator (disc_v) discriminates the image after decoding the randomly generated latent vector (l2); l2 is a latent vector randomly sampled from a uniform distribution [-1,1] and then processed by a tanh function; loss_ae_l is the latent space discriminator loss, which is the loss obtained by the latent space discriminator (disc_l) discriminating the latent vector (l1) output by the encoder; l1 is the latent vector obtained by encoding the real image with noise added through the encoder; loss_ssim is the SSIM structured coefficient loss between the reconstructed image and the input image.

[0066] In the specific implementation, the functions of the visual discriminator include:

[0067] 1. Judge the authenticity of the reconstructed image and distinguish between the original image and the reconstructed image;

[0068] 2. The input image (3 channels) is gradually reduced in dimension through a multi-layer convolutional network, and a discriminant score is finally output;

[0069] 3. Help the generator network generate more realistic images and form an adversarial learning mechanism;

[0070] 4. Finally, a score is output, and the output range is between 0-1 (using sigmoid activation), where 1 represents the real image and 0 represents the reconstructed / generated image.

[0071] Classifier:

[0072] It is used to determine whether an image is a real image or an abnormal image, and to perform binary classification on the input samples.

[0073] In the specific implementation process, this scheme introduces a horizontal text image screening mechanism in the construction of the training data set. For the leather signature data set, the key authentication target required by this scheme is arranged in the form of multiple lines of text, such as LOUIS VUITTON. Such letters are neatly arranged on the leather signature, but due to the shooting angle, the arrangement direction may be different from the horizontal angle. This will cause the key authentication area to be non-horizontal when it is sent to the network. Therefore, this scheme proposes a multi-line text screening algorithm that can run on the leather signature data set to screen out leather signatures with small horizontal deviations.

[0074] Specifically, the steps include:

[0075] Grayscale conversion:

[0076] I_gray(x,y)=0.299R(x,y)+0.587G(x,y)+0.114B(x,y)

[0077] Gaussian Blur:

[0078] I_blur(x,y)=I_gray(x,y)*G(x,y)

[0079] Where G(x,y) is the Gaussian kernel, defined as:

[0080] G(x,y)=(1 / (2πσ^2))*e^(-(x^2+y^2) / (2σ^2))

[0081] Canny edge detection:

[0082] Use the Canny algorithm to detect the edge of the blurred image and obtain the edge image I_edge(x,y).

[0083] Detecting lines using modified Hough transform: We use a modified probabilistic Hough transform to detect text lines. The standard Hough transform represents a line as:

[0084] ρ=x cos(θ)+y sin(θ)

[0085] Among them, ρ is the distance from the line to the origin, and θ is the angle between the normal of the line and the x-axis.

[0086] Instead of detecting all possible lines, the modified probabilistic Hough transform randomly samples edge points and only considers lines that pass through these points. This method is more suitable for detecting text lines because it is able to detect line segments of finite length.

[0087] For each detected line segment l_i, we calculate its tilt angle α_i:

[0088] α_i=arctan((y2_i-y1_i) / (x2_i-x1_i))*(180 / π)

[0089] Among them, (x1_i, y1_i) and (x2_i, y2_i) are the two endpoints of the line segment.

[0090] We only keep line segments with angles in the range [-45°, 45°], since we are mainly interested in nearly horizontal text lines:

[0091] That is, angle A={α_i|-45°≤α_i≤45°}

[0092] To remove outliers, we use the interquartile range (IQR) method:

[0093] Calculate the first quartile Q1 and the third quartile Q3 of A

[0094] Calculate the interquartile range: IQR = Q3-Q1

[0095] Define lower and upper bounds:

[0096] lower_bound=Q1-1.5*IQR

[0097] upper_bound=Q3+1.5*IQR

[0098] Filter Angle:

[0099] A_filtered={α_i∈A|lower_bound≤α_i≤upper_bound}.

[0100] In some embodiments of the present invention, in the step of calculating the difference value between the independent detection point segmented image and the reconstructed image of the positive sample and determining the detection result based on the difference value, the structural similarity index of the independent detection point segmented image and the reconstructed image of the positive sample is calculated, and the calculated structural similarity index is compared with a preset threshold to determine the detection result.

[0101] In some embodiments of the present invention, SSIM (Structure Similarity Index Measure) is a measure of the similarity between two images, which mainly considers three key features of the image: luminance, contrast, and structure.

[0102] In some embodiments of the present invention, the step of determining the preset threshold value includes:

[0103] The value range of the calculation threshold of AUC based on the training and test data sets; AUC (Area Under the Curve) is the area under the curve, usually refers to the area under the ROC (Receiver Operating Characteristic) curve, which is a commonly used evaluation indicator used to measure the classification performance of machine learning models under different thresholds;

[0104] Uniform values ​​are selected within the range of threshold values, and each threshold is tested using a test data set to obtain the model accuracy corresponding to each threshold. The threshold corresponding to the highest model accuracy is used as the final threshold.

[0105] In the process of calculating AUC:

[0106] Drawing of ROC curve: The ROC curve is obtained by calculating the true positive rate (TPR) and false positive rate (FPR) at different thresholds and drawing them. Each threshold corresponds to a point on the ROC curve;

[0107] Calculation of AUC value: The AUC value is obtained by calculating the area under the ROC curve. This area can be calculated by integrating the ROC curve, and the accuracy of the integration is related to the accuracy of the threshold. The more threshold values ​​are taken, the higher the accuracy of the result.

[0108] In summary, this solution uses a single classification approach and solves the positive and false identification of luxury goods as a single classification problem, removing the model's restrictions on the data set, thereby improving the model's recognition accuracy and robustness.

[0109] An embodiment of the present invention also provides a luxury goods authentication system based on a single classification, the system comprising a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0110] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps implemented by the aforementioned luxury goods authentication method based on a single classification are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0111] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0112] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0113] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying counterfeit luxury goods based on a single classification, characterized in that: The steps of the method include: Acquire an original leather swab sample image, and perform image enhancement preprocessing on the original leather swab sample image; Inputting the preprocessed original leather tag sample image into a pre-trained independent counterfeit detection point segmentation network, the independent counterfeit detection point segmentation network outputs a segmented independent counterfeit detection point segmentation image; Inputting the independent counterfeit detection point segmented image into a single classification model pre-trained with training data including only positive labels, the single classification model outputting a positive sample reconstructed image; The difference value between the independent detection point segmented image and the positive sample reconstructed image is calculated, and the detection result is determined based on the difference value.

2. The method for identifying counterfeit luxury goods based on a single classification according to claim 1, characterized in that: The step of performing image enhancement preprocessing on the original leather swab sample image includes performing image enhancement using a preset image enhancement algorithm, wherein the image enhancement algorithm includes gamma correction, an adaptive contrast enhancement algorithm, and background noise filtering.

3. The method for identifying counterfeit luxury goods based on a single classification according to claim 1, characterized in that: The method also includes the steps of: Using Hough transform to detect straight lines in the original leather tag sample image; The bevel angle of each straight line is calculated, and the data dispersion is calculated using the interquartile range method to determine whether the original leather swab sample image is qualified.

4. The method for identifying counterfeit luxury goods based on a single classification according to claim 1, characterized in that: The independent authentication point segmentation network is a YOLO v7 network.

5. The method for identifying counterfeit luxury goods based on a single classification according to claim 1, characterized in that: In the step of inputting the independent counterfeit detection point segmentation image into a single classification model that is pre-trained using training data that only includes positive labels, and the single classification model outputs a reconstructed image of a positive sample, the single classification model includes a fine-grained encoder and a fine-grained decoder, and the independent counterfeit detection point segmentation image is sequentially processed by the fine-grained encoder and the fine-grained decoder, and the reconstructed image of the positive sample is output.

6. The method for identifying counterfeit luxury goods based on a single classification according to claim 5, characterized in that: The fine-grained encoder includes a first encoding convolution layer, a first encoding activation function layer, a second encoding convolution layer, a first encoding normalization layer, a third encoding convolution layer, a second encoding normalization layer, a fourth encoding convolution layer, a second encoding activation function layer, a third encoding normalization layer, a first encoding pooling layer, a hyperbolic tangent activation function layer and a Flatten layer, which are sequentially connected.

7. The method for identifying counterfeit luxury goods based on a single classification according to claim 6, characterized in that: In the step of sequentially processing the independent false detection point segmentation image through a fine-grained encoder and a fine-grained decoder and outputting a reconstructed image of a positive sample, the independent false detection point segmentation image is input into the first encoding convolution layer of the fine-grained encoder, and a latent vector is output through a Flatten layer.

8. The method for identifying counterfeit luxury goods based on a single classification according to claim 7, characterized in that: The fine-grained decoder includes a sequentially connected zeroth decoding convolution layer, a zeroth decoding normalization layer, a first decoding convolution layer, a first decoding normalization layer, a second decoding convolution layer, a second decoding normalization layer, a third decoding convolution layer, a third decoding normalization layer, a Dropout layer, a fourth decoding convolution layer and a hyperbolic tangent activation function layer.

9. The method for identifying counterfeit luxury goods based on a single classification according to any one of claims 1 to 8, characterized in that: In the step of calculating the difference value between the independent detection point segmented image and the reconstructed image of the positive sample, and determining the detection result based on the difference value, the structural similarity index of the independent detection point segmented image and the reconstructed image of the positive sample is calculated, and the calculated structural similarity index is compared with a preset threshold to determine the detection result.

10. A luxury goods authentication system based on a single classification, characterized in that: The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions. The processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method as described in any one of claims 1 to 9.

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