Radio frequency fingerprint identification method based on double-flow feature embedding and consistency regularization

Abstract

The invention provides a radio frequency fingerprint identification method based on double-flow feature embedding and consistency regularization, and the method sequentially comprises the steps: obtaining and constructing a wireless signal data set with a label, and separating content information from hardware features; importing the wireless signal data set into a double-flow feature embedding module, and extracting hardware features of an original signal and a shielding signal; calculating cross entropy classification loss and regularization loss, and ensuring feature extraction and distribution consistency; parameters of the double-flow feature embedding module are optimized based on a comprehensive loss function; dynamically expanding a double-flow feature embedding module, and adding a new category classifier to adapt to new equipment; and deploying the model to carry out online identification, and updating the model weight in real time. According to the method, the recognition performance and robustness of the RFF in a content tampering scene are effectively enhanced through a robust learning framework guided by consistency.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication security, and in particular to a radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization, and belongs to the technical field of radio frequency fingerprint recognition (RFF). Background Art

[0002] Radio Frequency Fingerprinting (RFF) technology uses the physical layer characteristics of wireless transmitting devices (such as tiny manufacturing differences in hardware) to perform identity authentication, which has strong security and unforgeability. However, with the development of deep learning technology, existing RFF methods mainly rely on deep neural networks to automatically extract and classify signal features. Although these methods have performed well in improving recognition accuracy, they have also raised new problems, especially the "content dependency" problem. When the wireless signal contains specific content information (such as MAC addresses or other identifiable content), deep learning models tend to rely on this information for classification, thereby ignoring the hardware characteristics of the device. This content dependency reduces the robustness and security of the model against content tampering or forgery attacks. Summary of the invention

[0003] The purpose of the present invention is to solve the content dependency problem in the existing RF fingerprint recognition method and provide a content-independent RF fingerprint recognition method. By introducing a consistency-guided robust learning framework, the present invention not only improves the accuracy of recognition, but also enhances the robustness of the model in the face of content tampering, so that RFF technology can play a more effective role in more complex application scenarios.

[0004] The specific plan is as follows:

[0005] A radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization, comprising:

[0006] Step 1: Obtain and construct a labeled wireless signal dataset to separate content information and hardware features;

[0007] Step 2: Import the wireless signal dataset into the dual-stream feature embedding module to extract the hardware features of the original signal and the shielded signal;

[0008] Step 3: Calculate the cross entropy classification loss and regularization loss to ensure feature extraction and distribution consistency;

[0009] Step 4: Optimize the parameters of the two-stream feature embedding module based on the comprehensive loss function;

[0010] Step 5: Dynamically expand the two-stream feature embedding module and add new category classifiers to adapt to new devices;

[0011] Step 6: Deploy the model for online recognition and update the model weights in real time.

[0012] Furthermore, in step 1, obtaining and constructing a labeled wireless signal dataset, separating content information and hardware features includes:

[0013] Step 1.1: Collect the original wireless signal from the wireless transmitting device, including the real part and the imaginary part of the signal;

[0014] Step 1.2: According to the transmission protocol and structure of the signal, the content information (such as MAC address, device ID, etc.) in the signal is parsed to extract these specific contents;

[0015] Step 1.3: Preprocess the signal data to remove or mask specific content information so that only features related to the transmitting device hardware are retained in the signal;

[0016] Step 1.4: According to the device label and signal category, the signal samples are classified into positive samples (without specific content) and negative samples (with specific content), and a labeled wireless signal dataset is constructed, where the positive sample is the original signal stream and the negative sample is the shielded signal stream.

[0017] The core of this step is to ensure that the specific content in the signal data is separated from the hardware fingerprint features and to construct a standardized labeled dataset to support subsequent feature extraction and classification tasks.

[0018] Furthermore, in step 2, the wireless signal dataset is imported into the dual-stream feature embedding module, and the hardware features of the original signal and the shielded signal are extracted, including:

[0019] Step 2.1: Import the wireless signal dataset constructed in step 1 (including the original signal stream and the shielded signal stream) into the dual-stream feature embedding module for processing;

[0020] Step 2.2: The dual-stream feature embedding module processes the original signal stream and the shielded signal stream independently to extract their hardware features. For the shielded signal stream, the content shielding matrix is ​​applied to remove specific content before feature extraction;

[0021] Step 2.3: Output two feature vectors, representing the characteristics of the original signal stream And the feature z′ of the masked signal flow provides data for subsequent regularization;

[0022] The dual-stream feature embedding module ensures that the model can process both the original signal and the content-shielded signal, extract feature information related to the device hardware, and lay the foundation for reducing dependence on specific content.

[0023] Furthermore, in step 3, the cross entropy classification loss and regularization loss are calculated to ensure feature extraction and distribution consistency including:

[0024] Step 3.1: Substitute the feature vector z of the original signal stream and the corresponding label value into the cross entropy classification loss function, calculate the classification accuracy, and obtain the classification loss value L CE ;

[0025] Cross-Entropy Loss: Cross-entropy loss is used to measure the difference between the predicted distribution and the true distribution. The formula is as follows:

[0026]

[0027] Where N is the number of samples, C is the number of categories, and y i，c is the true label (0 or 1) of sample i i，c is the predicted probability that sample i corresponds to category c.

[0028] Step 3.2: Substitute the feature vectors z and z′ of the original signal stream and the masked signal stream into the semantic consistency regularization (SCR) module, use the maximum mean difference (MMD) technique to compare the feature distributions of the two, calculate the distribution difference, and obtain the semantic consistency regularization loss value L SCR ;

[0029] Maximum Mean Discrepancy (MMD): MMD is used to measure the difference between two distributions, especially for unsupervised learning. Its calculation formula is:

[0030]

[0031] Wherein, k(x, y) is a kernel function, such as a Gaussian kernel.

[0032] Step 3.3: Generate the attention matrices A and A′ of the original signal stream and the masked signal stream through the Class Activation Mapping (CAM) technique, align their distributions through the MMD technique, and calculate the attention consistency regularization loss L ACR ;

[0033] Class Activation Mapping (CAM): CAM is used to visualize the area of ​​interest of the CNN model on the input image. The calculation formula is as follows:

[0034]

[0035] Among them, w k is the weight of the last fully connected layer, A k (x) is the feature map of the last convolutional layer.

[0036] In this step, by calculating the cross entropy loss and regularization loss, we ensure that the model can reduce its dependence on specific content through consistency regularization while maintaining classification accuracy, thereby increasing the model's attention to hardware features.

[0037] Furthermore, in step 4, the optimization of the parameters of the dual-stream feature embedding module based on the comprehensive loss function includes:

[0038] Step 4.1: Construct the overall loss function, including the classification loss L CE , semantic consistency regularization loss L SCR and attention consistency regularization loss L ACR , the formula is:

[0039] L overall =L CE +λ1L SCR +λ2L ACR

[0040] Among them, λ1 and λ2 are the weights for adjusting each loss term;

[0041] Step 4.2: Optimize the model parameters through the gradient descent algorithm, and update the weights of the two-stream feature embedding module based on the minimization of the overall loss function until the loss value converges and the optimal model is obtained;

[0042] This step minimizes the comprehensive loss function and optimizes the model parameters, so that the model can effectively reduce content dependence and improve its robust recognition ability of hardware features while maintaining classification accuracy.

[0043] Furthermore, in step 5, the dual-stream feature embedding module is dynamically expanded and new category classifiers are added to adapt to new devices, including:

[0044] Step 5.1: When a new wireless transmitting device or signal category is detected, dynamically expand the model, add a new category label, and generate a device fingerprint classifier that adapts to the new device;

[0045] Step 5.2: In the two-stream feature embedding module, keep the stability module unchanged and introduce the classification ability of new categories through the plasticity module to enhance the recognition of new devices;

[0046] Step 5.3: The dynamically expanded plasticity module continuously improves its adaptability to new devices and new signal types through real-time learning of new signal samples;

[0047] By dynamically expanding new category classifiers, the model has the ability to learn and adapt to new device fingerprints in real time, maintaining efficient recognition of new devices and enhancing the scalability and versatility of the model.

[0048] Furthermore, in step 6, the model is deployed for online recognition, and the model weights are updated in real time, including:

[0049] Step 6.1: Deploy the optimal two-stream feature embedding model into the online wireless identification system to process the received wireless signal data in real time and perform transmitter identity authentication;

[0050] Step 6.2: Based on the prediction results output by the model, determine whether the input signal comes from a known device. If a new device signal is detected, adaptively learn and classify the new signal;

[0051] Step 6.3: Dynamically update the model weights through the online training mechanism to ensure that the system can adapt to new devices and signal content changes in a timely manner, improve the robustness of recognition and the security of the system;

[0052] This step ensures that the model can continue to learn and self-optimize in a real-time wireless signal environment, maintain efficient recognition capabilities, ensure that the system can adapt to device changes, and enhance the stability and security of the system.

[0053] The beneficial effects of the present invention are:

[0054] High robustness: By reducing the dependence on specific content, the method of the present invention significantly improves the recognition accuracy and robustness of the model in content tampering scenarios, avoiding the defect that the traditional RFF method is easily affected by content tampering.

[0055] Improved recognition performance: Combining dual-stream feature embedding and consistency regularization technology, the method of the present invention can better extract hardware fingerprint features in the signal, thereby improving the overall recognition performance of the model.

[0056] Wide applicability: The method of the present invention is applicable to a variety of wireless communication systems, and has important application value in systems involving specific content (such as WiFi, ADS-B, etc.). BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the structure of a robust learning framework based on consistency guidance provided by an embodiment of the present invention.

[0058] Figure 2 It is a structural diagram of the dual-stream feature embedding module, showing the feature extraction process of the original signal stream and the content-masked signal stream. DETAILED DESCRIPTION

[0059] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0060] As shown in the figure, the present invention provides a radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization, including:

[0061] Step 1: Obtain and construct a labeled wireless signal dataset to separate content information and hardware features;

[0062] Step 2: Import the wireless signal dataset into the dual-stream feature embedding module to extract the hardware features of the original signal and the shielded signal;

[0063] Step 3: Calculate the cross entropy classification loss and regularization loss to ensure feature extraction and distribution consistency;

[0064] Step 4: Optimize the parameters of the two-stream feature embedding module based on the comprehensive loss function;

[0065] Step 5: Dynamically expand the two-stream feature embedding module and add new category classifiers to adapt to new devices;

[0066] Step 6: Deploy the model for online recognition and update the model weights in real time.

[0067] In this embodiment, obtaining and constructing a labeled wireless signal dataset in step 1, and separating content information from hardware features includes:

[0068] Step 1.1: Collect the original wireless signal from the wireless transmitting device, including the real part and the imaginary part of the signal;

[0069] Step 1.2: According to the transmission protocol and structure of the signal, the content information (such as MAC address, device ID, etc.) in the signal is parsed to extract these specific contents;

[0070] Step 1.3: Preprocess the signal data to remove or mask specific content information so that only features related to the transmitting device hardware are retained in the signal;

[0071] Step 1.4: According to the device label and signal category, the signal samples are classified into positive samples (without specific content) and negative samples (with specific content), and a labeled wireless signal dataset is constructed, where the positive sample is the original signal stream and the negative sample is the shielded signal stream.

[0072] The core of this step is to ensure that the specific content in the signal data is separated from the hardware fingerprint features and to construct a standardized labeled dataset to support subsequent feature extraction and classification tasks.

[0073] In this embodiment, in step 2, the wireless signal data set is imported into the dual-stream feature embedding module, and the hardware features of the original signal and the shielded signal are extracted, including:

[0074] Step 2.1: Import the wireless signal dataset constructed in step 1 (including the original signal stream and the shielded signal stream) into the dual-stream feature embedding module for processing;

[0075] Step 2.2: The dual-stream feature embedding module processes the original signal stream and the shielded signal stream independently to extract their hardware features. For the shielded signal stream, the content shielding matrix is ​​applied to remove specific content before feature extraction;

[0076] Step 2.3: Output two feature vectors, which represent the feature z of the original signal stream and the feature z′ of the shielded signal stream, respectively, to provide data for subsequent regularization;

[0077] The dual-stream feature embedding module ensures that the model can process both the original signal and the content-shielded signal, extract feature information related to the device hardware, and lay the foundation for reducing dependence on specific content.

[0078] In this embodiment, the cross entropy classification loss and regularization loss are calculated in step 3 to ensure feature extraction and distribution consistency, including:

[0079] Step 3.1: Substitute the feature vector z of the original signal stream and the corresponding label value into the cross entropy classification loss function, calculate the classification accuracy, and obtain the classification loss value L CE ;

[0080] Step 3.2: Substitute the feature vectors z and z′ of the original signal stream and the masked signal stream into the semantic consistency regularization (SCR) module, use the maximum mean difference (MMD) technique to compare the feature distributions of the two, calculate the distribution difference, and obtain the semantic consistency regularization loss value L SCR ;

[0081] Step 3.3: Generate the attention matrices A and A′ of the original signal stream and the masked signal stream through the Class Activation Mapping (CAM) technique, align their distributions through the MMD technique, and calculate the attention consistency regularization loss L ACR ;

[0082] In this step, by calculating the cross entropy loss and regularization loss, we ensure that the model can reduce its dependence on specific content through consistency regularization while maintaining classification accuracy, thereby increasing the model's attention to hardware features.

[0083] In this embodiment, the optimization of the parameters of the dual-stream feature embedding module based on the comprehensive loss function in step 4 includes:

[0084] Step 4.1: Construct the overall loss function, including the classification loss L CE , semantic consistency regularization loss L SCR and attention consistency regularization loss L ACR, the formula is:

[0085] L overall =L CE +λ1L SCR +λ2L ACR

[0086] Among them, λ1 and λ2 are the weights for adjusting each loss term;

[0087] Step 4.2: Optimize the model parameters through the gradient descent algorithm, and update the weights of the two-stream feature embedding module based on the minimization of the overall loss function until the loss value converges and the optimal model is obtained;

[0088] This step minimizes the comprehensive loss function and optimizes the model parameters, so that the model can effectively reduce content dependence and improve its robust recognition ability of hardware features while maintaining classification accuracy.

[0089] In this embodiment, in step 5, the dual-stream feature embedding module is dynamically expanded, and a new category classifier is added to adapt to the new device, including:

[0090] Step 5.1: When a new wireless transmitting device or signal category is detected, dynamically expand the model, add a new category label, and generate a device fingerprint classifier that adapts to the new device;

[0091] Step 5.2: In the two-stream feature embedding module, keep the stability module unchanged and introduce the classification ability of new categories through the plasticity module to enhance the recognition of new devices;

[0092] Step 5.3: The dynamically expanded plasticity module continuously improves its adaptability to new devices and new signal types through real-time learning of new signal samples;

[0093] By dynamically expanding new category classifiers, the model has the ability to learn and adapt to new device fingerprints in real time, maintaining efficient recognition of new devices and enhancing the scalability and versatility of the model.

[0094] In this embodiment, deploying the model for online recognition in step 6 and updating the model weights in real time include:

[0095] Step 6.1: Deploy the optimal two-stream feature embedding model into the online wireless identification system to process the received wireless signal data in real time and perform transmitter identity authentication;

[0096] Step 6.2: Based on the prediction results output by the model, determine whether the input signal comes from a known device. If a new device signal is detected, adaptively learn and classify the new signal;

[0097] Step 6.3: Dynamically update the model weights through the online training mechanism to ensure that the system can adapt to new devices and signal content changes in a timely manner, improve the robustness of recognition and the security of the system;

[0098] This step ensures that the model can continue to learn and self-optimize in a real-time wireless signal environment, maintain efficient recognition capabilities, ensure that the system can adapt to device changes, and enhance the stability and security of the system.

[0099] The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above-mentioned implementation mode, but also include technical schemes composed of any combination of the above-mentioned technical features. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization, characterized in that: include: Step 1: Obtain and construct a labeled wireless signal dataset and separate content information from hardware features; Step 2: Import the wireless signal dataset into the dual-stream feature embedding module to extract the hardware features of the original signal and the shielded signal; Step 3: Calculate the cross entropy classification loss and regularization loss to ensure feature extraction and distribution consistency; Step 4: Optimize the parameters of the two-stream feature embedding module based on the comprehensive loss function; Step 5: Dynamically expand the two-stream feature embedding module and add new category classifiers to adapt to new devices; Step 6: Deploy the model for online recognition and update the model weights in real time.

2. According to claim 1, a radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization is characterized in that: Step 1 specifically includes: Step 1.1: Collect the original wireless signal from the wireless transmitting device, including the real part and the imaginary part of the signal; Step 1.2: According to the transmission protocol and structure of the signal, the content information in the signal is parsed to extract the specific content; Step 1.3: Preprocess the signal to remove or mask specific content information so that only features related to the hardware of the transmitting device are retained in the signal; Step 1.4: According to the device label and signal category, the signal is classified into positive samples and negative samples, and a labeled wireless signal dataset is constructed, where the positive sample does not contain specific content and is the original signal stream, and the negative sample contains specific content and is the shielded signal stream.

3. The radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization according to claim 2 is characterized in that: Step 2 specifically includes: Step 2.1: Import the wireless signal dataset constructed in step 1 into the dual-stream feature embedding module for processing; Step 2.2: The dual-stream feature embedding module processes the original signal stream and the shielded signal stream independently to extract their hardware features respectively; for the shielded signal stream, the content shielding matrix is ​​applied to remove specific content before feature extraction; Step 2.3: Output two feature vectors, representing the feature z of the original signal stream and the feature z′ of the shielded signal stream, respectively, to provide data for subsequent regularization.

4. The radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization according to claim 3 is characterized in that: Step 3 specifically includes: Step 3.1: Substitute the feature vector z of the original signal stream and the corresponding label value into the cross entropy classification loss function, calculate the classification accuracy, and obtain the classification loss value L CE ; Cross entropy classification loss function: Cross entropy loss is suitable for measuring the difference between the predicted distribution and the true distribution. The formula is as follows: Where N is the number of samples, C is the number of categories, and y i，c is the true label of sample i, p i，c is the predicted probability that sample i corresponds to category c; Step 3.2: Substitute the feature vectors z and z′ of the original signal stream and the shielded signal stream into the semantic consistency regularization SCR module, use the maximum mean difference MMD technology to compare the feature distributions of the two, calculate the distribution difference, and obtain the semantic consistency regularization loss value L SCR ; Maximum mean difference: used to measure the difference between two distributions, especially for unsupervised learning, and its calculation formula is: Among them, k(x, y) is the kernel function; Step 3.3: Generate the attention matrices A and A′ of the original signal stream and the masked signal stream through the class activation mapping CAM technique, align their distributions through the MMD technique, and calculate the attention consistency regularization loss L ACR ; Class activation map: It is used to visualize the area of ​​interest of the CNN model on the input image. The calculation formula is as follows: Among them, w k is the weight of the last fully connected layer, A k (x) is the feature map of the last convolutional layer.

5. The radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization according to claim 4 is characterized in that: Step 4 specifically includes: Step 4.1: Construct the overall loss function, including the classification loss L CE , semantic consistency regularization loss L SCR and attention consistency regularization loss L ACR , the formula is: L overall =L CE +λ1L SCR +λ2L ACR Among them, λ1 and λ2 are the weights for adjusting each loss term; Step 4.2: Optimize the model parameters through the gradient descent algorithm, and update the weights of the two-stream feature embedding module based on the minimization of the overall loss function until the loss value converges to obtain the optimal model.

6. The radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization according to claim 5 is characterized in that: Step 5 specifically includes: Step 5.1: When a new wireless transmitting device or signal category is detected, dynamically expand the model, add a new category label, and generate a device fingerprint classifier that adapts to the new device; Step 5.2: In the two-stream feature embedding module, keep the stability module unchanged and introduce the classification ability of new categories through the plasticity module to enhance the recognition of new devices; Step 5.3: The dynamically expanded plasticity module continuously improves its adaptability to new devices and new signal types through real-time learning of new signal samples.

7. The radio frequency fingerprint recognition method based on dual-stream feature embedding and consistency regularization according to claim 6 is characterized in that: Step 6 specifically includes: Step 6.1: Deploy the optimal two-stream feature embedding model into the online wireless identification system to process the received wireless signal data in real time and perform transmitter identity authentication; Step 6.2: Based on the prediction results output by the model, determine whether the input signal comes from a known device. If a new device signal is detected, adaptively learn and classify the new signal; Step 6.3: Dynamically update the model weights through the online training mechanism to ensure that the system can adapt to new devices and signal content changes in a timely manner, thereby improving the robustness of recognition and the security of the system.

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