Mass remote sensing image information hiding processing method, system and device

Abstract

The invention relates to the technical field of image encryption, in particular to a massive remote sensing image information hiding processing method, system and device, and the method comprises the steps: obtaining a basic image; embedding at least one remote sensing image into the basic image by using a multi-encryption technology to form a conversion image; performing recovery processing on the conversion image to extract an embedded remote sensing image; and checking whether the recovered remote sensing image information is complete and lossless. Therefore, safe transmission of the information is ensured, and meanwhile, the receiver can be ensured to recover the information accurately.

Description

Technical Field

[0001] The present invention relates to the field of image encryption technology, and in particular to a method for hiding and processing massive remote sensing image information and a system and device thereof. Background Art

[0002] Remote sensing data encryption refers to the process of encrypting data collected from satellites or other remote sensing platforms to protect the security of these data during transmission and storage. Remote sensing data usually contains a large amount of geographic information, such as topography, vegetation cover, land use, etc. This information may be sensitive or commercially valuable, so measures need to be taken to prevent unauthorized access or tampering.

[0003] Common encryption algorithms include symmetric encryption (such as AES) and asymmetric encryption (such as RSA). Symmetric encryption algorithms use the same key for encryption and decryption, which is fast but complex to manage; asymmetric encryption algorithms use a pair of public and private keys, which are more secure but slower to process.

[0004] However, the camouflage effect of this method is not strong and it is easy to be identified and seized. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for hiding and processing massive remote sensing image information, and a device thereof, so as to hide remote sensing image information through surface images to improve security.

[0006] To achieve the above-mentioned object, in a first aspect, the present invention provides a method for hiding information in massive remote sensing images, comprising obtaining a basic image; embedding at least one remote sensing image into the base image using a multiple encryption technique to form a transformed image; Performing restoration processing on the converted image to extract the embedded remote sensing image; Check whether the restored remote sensing image information is intact.

[0007] The specific steps of embedding at least one remote sensing image into the base image using the multiple encryption technology to form a converted image include: Performing data compression processing on the remote sensing image before embedding the information; The compressed remote sensing image is encrypted using a symmetric encryption algorithm to generate encrypted data; Use an asymmetric encryption algorithm to encrypt the once encrypted data again to generate secondary encrypted data; The secondary encrypted data is embedded into the pixel values ​​of the base image using Least Significant Bit substitution.

[0008] The specific steps of performing data compression processing on the remote sensing image before embedding information include: Use filters to remove noise from images; Use principal component analysis to reduce dimensionality and retain the main features in the image; The transformed image coefficients are simplified to a finite number of discrete values ​​by quantization; Use entropy coding methods to further compress image data; The compressed image data is packed after compression.

[0009] The specific steps of using the least significant bit replacement to embed the secondary encrypted data into the pixel value of the base image include: Divide the encrypted secondary encrypted data into a number of bits; Convert the base image in RGB color space to a format where each pixel consists of three channels: red, green, and blue; Check the least significant bit of the RGB channels of each pixel, for each bit data to be embedded, find the corresponding pixel, and replace at least one least significant bit in the RGB channels of these pixels; After replacing all the data that needs to be embedded, the converted image is obtained.

[0010] Wherein, after replacing all the data to be embedded and obtaining the converted image, the step further includes: The peak signal-to-noise ratio metric is used to evaluate the degree of change between the transformed image and the base image.

[0011] The specific steps of using the peak signal-to-noise ratio index to evaluate the degree of change between the converted image and the base image include: obtaining the original base image and the processed converted image; Calculate the mean square error between the base image and the transformed image; Determine the maximum pixel value based on the bit depth of the image; The peak signal-to-noise ratio is calculated based on the mean square error and the maximum pixel value.

[0012] The specific steps of restoring the converted image to extract the embedded remote sensing image information include: Load the converted image with embedded information; For each pair of corresponding pixels of the original image and the converted image, check the least significant bit in the RGB channels; For each selected pixel, read the least significant bit of its RGB value and reassemble it into a complete binary data stream; Convert binary data stream back to original format; Use the corresponding decryption algorithm and key to decrypt and extract the decrypted remote sensing image.

[0013] In a second aspect, the present invention also provides a massive remote sensing image information hiding processing device, including a basic image acquisition module, an image conversion module, a decryption module and an integrity verification module; The basic image acquisition module is used to acquire a basic image; The image conversion module is used to embed at least one remote sensing image into the base image using a multiple encryption technique to form a converted image; The decryption module is used to perform a recovery process on the converted image to extract the embedded remote sensing image; The integrity verification module is used to check whether the restored remote sensing image information is complete and intact.

[0014] In a third aspect, the present invention further provides a massive remote sensing image information hiding and processing system, comprising the massive remote sensing image information hiding and processing device.

[0015] The method and system and device for hiding information of massive remote sensing images of the present invention first need to select one or more images as carrier images, i.e., base images. These images can be of any type as long as they are suitable for carrying additional information. The selection of base images should take into account factors such as their size, resolution, and whether they are easy to attract attention. One or more encryption algorithms are used to protect the remote sensing image data to be embedded in the base image. Encryption can ensure that even if a third party obtains the image, the secret information therein cannot be easily read. Then, the encrypted remote sensing image data is embedded into the base image through digital watermarking technology or other information hiding technology to generate a new image, i.e., a converted image. This process may involve technical means such as frequency domain transformation, spatial domain operation, or mixed domain processing. When it is necessary to extract the original remote sensing image data from the converted image, the same or compatible decryption algorithm as before must be used to decrypt and restore the hidden data. This process may also require the use of a specific algorithm to identify and separate the embedded information and separate it from the carrier image. The last step is to verify whether the extracted remote sensing image remains in its original state without any damage. This can be achieved by comparing the integrity of the image data before and after restoration, such as calculating hash values ​​or using other data verification methods. The entire process is designed to ensure that information is transmitted securely while ensuring that the receiver can accurately restore the information. This method has broad application prospects in scientific research and business. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of the method for hiding information of massive remote sensing images of the present invention.

[0018] Figure 2 The present invention is a flow chart of using multiple encryption technology to embed at least one remote sensing image into the base image to form a converted image.

[0019] Figure 3 It is a flow chart of the present invention for performing data compression processing on the remote sensing image before embedding information.

[0020] Figure 4 It is a flowchart of the present invention for embedding secondary encrypted data into the pixel value of the base image using the Least Significant Bit replacement.

[0021] Figure 5 It is a flow chart of using the peak signal-to-noise ratio indicator to evaluate the degree of change between the converted image and the basic image of the present invention.

[0022] Figure 6 The present invention is a flowchart of restoring the converted image to extract the embedded remote sensing image. DETAILED DESCRIPTION

[0023] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0024] See also Figure 1 to Figure 6 The present invention provides a method for hiding information of massive remote sensing images, comprising: S101 obtains a basic image; The base image can be a landscape image to better embed and conceal the remote sensing image.

[0025] S102 uses a multiple encryption technique to embed at least one remote sensing image into the base image to form a converted image; The specific steps include: S201 performs data compression processing on the remote sensing image before embedding information; The specific steps include: S301 uses a filter to remove noise from the image; Use appropriate filter technology to remove noise from the image. This process aims to improve image quality and make the image clearer and more discernible by eliminating or reducing unwanted signal interference. Commonly used filters include median filtering and mean filtering, which can effectively remove salt and pepper noise or Gaussian noise, thereby providing a cleaner data basis for subsequent processing.

[0026] S302 uses principal component analysis to reduce dimensionality and retain the main features in the image; Next, principal component analysis (PCA) is used to reduce the dimensionality of the image data. PCA is a statistical method used to extract the main features in an image and convert them into a new set of orthogonal variables, namely principal components. This not only reduces the redundancy of the data set, but also maintains the key information of the image, which is very beneficial for subsequent data analysis and pattern recognition.

[0027] S303 simplifies the transformed image coefficients into a finite number of discrete values ​​by quantization; By quantizing the transformed image coefficients, these continuous values ​​are simplified to a finite number of discrete values. Quantization is a key step that further compresses the data by reducing the number of bits required to represent the image, but it also introduces some distortion. In order to balance the relationship between compression ratio and image quality, a suitable quantization table is usually selected for this operation.

[0028] S304 further compresses the image data using an entropy coding method; Entropy coding is used to further compress image data. Entropy coding is a lossless data compression method that uses the probability of symbol occurrence to represent frequently occurring symbols with shorter bit strings, while less frequently occurring symbols are represented with longer bit strings. The entropy coding algorithms that can be used are Huffman coding and arithmetic coding, which can effectively reduce the file size without losing any information.

[0029] S305 encapsulates the compressed image data after compression.

[0030] After completing all the above compression steps, the compressed image data needs to be encapsulated for storage or transmission. The encapsulation process usually involves packaging the compressed data and possibly adding necessary metadata (such as information about the compression algorithm) so that the receiving end can correctly decompress and use the data.

[0031] S202 uses a symmetric encryption algorithm to encrypt the compressed remote sensing image to generate primary encrypted data; The characteristic of symmetric encryption algorithm is that the same key is used for encryption and decryption. This encryption method is usually fast and suitable for fast encryption of large amounts of data. Symmetric encryption algorithms that can be used include Advanced Encryption Standard (AES), Data Encryption Standard (DES) and its variant Triple DES (3DES). By selecting a symmetric encryption algorithm suitable for the current application scenario, image data can be effectively protected from unauthorized access. When applying symmetric encryption algorithms, it is necessary to ensure the secure management of keys, because once the keys are leaked, the encrypted data may be illegally accessed.

[0032] S203 uses an asymmetric encryption algorithm to re-encrypt the once encrypted data to generate secondary encrypted data; The characteristic of asymmetric encryption algorithms is that different keys, namely public keys and private keys, are used for encryption and decryption. The public key can be published publicly, while the private key must be kept confidential. When data is encrypted with a public key, only those who have the corresponding private key can decrypt it. Although this method has a slower encryption and decryption speed, it provides higher security. Commonly used asymmetric encryption algorithms include RSA and elliptic curve cryptography (ECC). In this step, the data that has been encrypted once is encrypted again by an asymmetric encryption algorithm to generate secondary encrypted data. This makes it impossible to decrypt without the correct private key even if the encrypted data is intercepted, which greatly enhances the security of the data.

[0033] S204 embeds the secondary encrypted data into the pixel value of the base image using the Least Significant Bit replacement.

[0034] The specific steps include: S401 divides the encrypted secondary encrypted data into a number of bits; The data that has been encrypted twice is divided into several bits. This is done to facilitate embedding these bits into the pixel values ​​of the image one by one.

[0035] S402 converts the basic image in RGB color space into a format in which each pixel consists of three channels: red, green, and blue; Convert the base image used as the carrier from its original color space (which may be RGB, CMYK or other color models) to an RGB color space format where each pixel consists of three color channels: red, green, and blue. This is done to facilitate processing of each color channel of each pixel separately.

[0036] S403 checks the least significant bit of the RGB channel of each pixel, finds the corresponding pixel for each bit data to be embedded, and replaces at least one least significant bit in the RGB channel of these pixels; For each bit of data to be embedded, find the corresponding pixel and replace at least one of the least significant bits in the RGB channels of these pixels with the data bit to be embedded. For example, if the data to be embedded is '1', then replace one or more of the least significant bits of the corresponding pixel with '1'. This process may slightly change the color value of the pixel, but since only the least significant bit is changed, it is difficult for the human eye to detect this change.

[0037] S404 obtains a converted image after replacing all data to be embedded.

[0038] Continue the above process until all the encrypted data is embedded into the pixel values ​​of the base image. When all the data bits to be embedded have been replaced into the least significant bits of the image, we get the final transformed image, also called a stego-image or watermark image.

[0039] S405 uses a peak signal-to-noise ratio indicator to evaluate the degree of change between the converted image and the base image.

[0040] The specific steps include: S501 obtains an original base image and a processed conversion image; Get the original base image without any processing and the transformed image after data embedding. These two images are the basis for comparison, the former represents the ideal situation and the latter is the actual result of the processing.

[0041] S502 calculates the mean square error between the base image and the converted image; Next, calculate the mean squared error (MSE) between the base image and the transformed image. The mean squared error is obtained by calculating the sum of the squares of the difference between the corresponding pixels of the two images and dividing it by the total number of pixels. The formula is: MSE=

[0042] Where I(i,j) represents the pixel value of the base image at position (i,j), K(i,j) represents the pixel value of the transformed image at the same position, and M and N are the height and width of the image respectively.

[0043] S503 determines the maximum pixel value according to the bit depth of the image; For 8-bit RGB images, the maximum value of each color channel is 255; for images with higher bit depths, the maximum value will increase accordingly. This maximum pixel value will be used to normalize the mean square error in subsequent calculations.

[0044] S504 calculates the peak signal-to-noise ratio based on the mean square error and the maximum pixel value.

[0045] PSNR is calculated using mean square error and maximum pixel value. PSNR is usually expressed in decibels (dB) and is calculated as:

[0046] in is the maximum possible pixel value in the image. For an 8-bit image, = 255; MSE is the mean square error calculated above.

[0047] S103 performs restoration processing on the converted image to extract the embedded remote sensing image; The specific steps include: S601 loads the converted image with embedded information; First, load the converted image that has the encrypted data embedded in it. This means that the image file needs to be read into the processing software or program so that it can be further analyzed and processed.

[0048] S602 checks the least significant bit in the RGB channels for each pair of corresponding pixels of the original image and the converted image; Since the data is embedded into the least significant bit of the pixel during the embedding phase, these bits need to be checked one by one in this phase to recover the data. For each color channel (red, green, blue) of each pixel, its least significant bit needs to be checked to see if it has been modified.

[0049] S603 reads the least significant bit of the RGB value of each selected pixel and reassembles it into a complete binary data stream; This process involves arranging the bits of information extracted from each pixel in sequence to form a continuous bit stream, which is the binary form of the originally embedded data.

[0050] S604 converts the binary data stream back to the original format; This involves decoding the binary data into its corresponding data type, such as the original format of the image file (JPEG, PNG, etc.). This process may need to be reversed depending on the specific encoding used when it was embedded.

[0051] S605 uses the corresponding decryption algorithm and key to decrypt the extracted remote sensing image.

[0052] The data has been encrypted before embedding, so now the correct decryption algorithm and key are needed to restore the data. This step ensures that only users with the correct key can access and understand the embedded data.

[0053] S104 checks whether the restored remote sensing image information is intact.

[0054] Check whether the restored image contains all the metadata information of the original image, including image size, resolution, color mode (such as RGB, grayscale, etc.), bit depth, etc. This step ensures that the basic properties of the image have not changed. Use consistency check tools for comparison. You can use specialized consistency check tools or write scripts to automatically compare the image data before and after restoration. These tools can help detect whether there are subtle differences between the two images, such as different pixel values. Common comparison methods include calculating the mean square error (MSE) between the two images, the structural similarity index (SSIM), etc.

[0055] Second embodiment Based on the first embodiment, the present invention also provides a massive remote sensing image information hiding and processing device, including a basic image acquisition module, an image conversion module, a decryption module and an integrity verification module; the basic image acquisition module is used to acquire a basic image; the image conversion module is used to use multiple encryption technology to embed at least one remote sensing image into the basic image to form a converted image; the decryption module is used to restore the converted image to extract the embedded remote sensing image; the integrity verification module is used to check whether the restored remote sensing image information is complete and intact.

[0056] In this embodiment, the basic image acquisition module acquires the basic image used to carry the hidden data. The basic image can come from various sources, such as drone photography, ground monitoring system, etc. The functions of the module include but are not limited to image downloading, importing, preprocessing (such as resizing, rotating, format conversion, etc.) and quality inspection to ensure that the selected image is suitable for subsequent processing steps.

[0057] The image conversion module uses multiple encryption techniques to embed at least one remote sensing image into a base image to form a converted image. Specifically, this process usually includes but is not limited to the following steps: compressing the remote sensing image to reduce its volume and optimize storage and transmission efficiency. Using a symmetric encryption algorithm to encrypt the compressed remote sensing image data for the first time. Using an asymmetric encryption algorithm to encrypt the encrypted data twice to enhance data security. Using the least significant bit (LSB) replacement technology to embed the encrypted data into the pixel value of the base image to form a converted image.

[0058] The decryption module performs recovery processing on the converted image to extract the remote sensing image embedded in it. The specific steps include: loading the converted image with embedded information. Checking the least significant bit of the RGB channel of each pixel in the converted image to identify the embedded data. Extracting the embedded data and reorganizing it into a complete binary stream. Converting the binary stream back to its original format. Decrypting the extracted data using the corresponding decryption algorithm and key to restore the original remote sensing image.

[0059] The integrity verification module is used to check whether the restored remote sensing image information is intact. This usually involves the following steps: Compare the image metadata before and after restoration to ensure that the basic attributes of the image have not changed.

[0060] Calculate and compare the mean square error (MSE) and peak signal-to-noise ratio (PSNR) between the restored image and the original image to quantify the difference in image quality. Perform a visual inspection to ensure that there is no visually noticeable loss in image quality. Verify that the restored image passes error detection mechanisms (e.g., checksums, hashes, etc.) to ensure that the data has not been corrupted during transmission.

[0061] Through the collaborative work of the above four modules, this device can effectively handle the information hiding and recovery tasks of massive remote sensing images, ensure the secure transmission and storage of data, and maintain the high quality of the images.

[0062] Third embodiment On the basis of the second embodiment, the present invention further provides a massive remote sensing image information hiding processing system, comprising the massive remote sensing image information hiding processing device.

[0063] The present invention also provides a massive remote sensing image information hiding processing system, which includes the aforementioned massive remote sensing image information hiding processing device. The system is designed to efficiently manage and process a large amount of remote sensing image data, and ensure the secure transmission and storage of data through information hiding technology.

[0064] In addition to the above devices, the system is also equipped with a management platform to coordinate the workflow of each module and provide a user interface. The management platform supports functions such as uploading and downloading images, managing encryption keys, and setting system configurations to ensure the smooth operation of the entire system. The system also includes a dedicated data storage and transmission module to handle the secure storage and efficient transmission of massive image data. This module supports distributed storage solutions, can cope with the storage needs of large-scale data, and uses security protocols to ensure the integrity and confidentiality of data during transmission.

[0065] The system provides a user interface and service terminal, which allows users to conveniently upload original images, retrieve processed images, and manage personal account information. These interfaces are designed to be user-friendly and easy to operate, allowing users to easily use the various services provided by the system.

[0066] By integrating the above components, this system can effectively manage and process massive remote sensing image data, use information hiding technology to ensure data security, and ensure that image quality is not compromised through a strict integrity verification mechanism. The system is suitable for various application scenarios that require high security and reliability, such as environmental monitoring, urban planning and other fields.

[0067] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for hiding information in massive remote sensing images, characterized in that: include: Get the base image; embedding at least one remote sensing image into the base image using a multiple encryption technique to form a transformed image; Performing restoration processing on the converted image to extract the embedded remote sensing image; Check whether the restored remote sensing image information is intact.

2. The method for hiding information in massive remote sensing images according to claim 1, characterized in that: The specific steps of embedding at least one remote sensing image into the base image using the multiple encryption technology to form a converted image include: Performing data compression processing on the remote sensing image before embedding the information; The compressed remote sensing image is encrypted using a symmetric encryption algorithm to generate encrypted data; Use an asymmetric encryption algorithm to encrypt the once encrypted data again to generate secondary encrypted data; The secondary encrypted data is embedded into the pixel values ​​of the base image using Least Significant Bit substitution.

3. The method for hiding information in massive remote sensing images as claimed in claim 2, characterized in that: The specific steps of performing data compression processing on the remote sensing image before embedding information include: Use filters to remove noise from images; Use principal component analysis to reduce dimensionality and retain the main features in the image; The transformed image coefficients are simplified to a finite number of discrete values ​​by quantization; Use entropy coding methods to further compress image data; The compressed image data is packed after compression.

4. The method for hiding information in massive remote sensing images as claimed in claim 3, characterized in that: The specific steps of using the least significant bit replacement to embed the secondary encrypted data into the pixel value of the base image include: Divide the encrypted secondary encrypted data into a number of bits; Convert the base image in RGB color space to a format where each pixel consists of three channels: red, green, and blue; Check the least significant bit of the RGB channels of each pixel, for each bit data to be embedded, find the corresponding pixel, and replace at least one least significant bit in the RGB channels of these pixels; After replacing all the data that needs to be embedded, the converted image is obtained.

5. The method for hiding information in massive remote sensing images as claimed in claim 4, characterized in that: After all the data to be embedded are replaced and the converted image is obtained, the steps further include: The peak signal-to-noise ratio metric is used to evaluate the degree of change between the transformed image and the base image.

6. The method for hiding information in massive remote sensing images as claimed in claim 5, characterized in that: The specific steps of using the peak signal-to-noise ratio indicator to evaluate the degree of change between the converted image and the base image include: Get the original base image and the processed transformed image; Calculate the mean square error between the base image and the transformed image; Determine the maximum pixel value based on the bit depth of the image; The peak signal-to-noise ratio is calculated based on the mean square error and the maximum pixel value.

7. The method for hiding information in massive remote sensing images as claimed in claim 6, characterized in that: The specific steps of performing restoration processing on the converted image to extract the embedded remote sensing image information include: Load the converted image with embedded information; For each pair of corresponding pixels of the original image and the converted image, check the least significant bit in the RGB channels; For each selected pixel, read the least significant bit of its RGB value and reassemble it into a complete binary data stream; Convert binary data stream back to original format; Use the corresponding decryption algorithm and key to decrypt and extract the decrypted remote sensing image.

8. A massive remote sensing image information hiding processing device, using the massive remote sensing image information hiding processing method according to any one of claims 1 to 7, characterized in that: It includes basic image acquisition module, image conversion module, decryption module and integrity verification module; The basic image acquisition module is used to acquire a basic image; The image conversion module is used to embed at least one remote sensing image into the base image using a multiple encryption technique to form a converted image; The decryption module is used to perform a recovery process on the converted image to extract the embedded remote sensing image; The integrity verification module is used to check whether the restored remote sensing image information is complete and intact.

9. A massive remote sensing image information hiding and processing system, characterized in that: It comprises a massive remote sensing image information hiding processing device as described in claim 8.

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