Image anti-counterfeiting technology based on software algorithm

Abstract

The invention relates to the technical field of image anti-counterfeiting, in particular to an image anti-counterfeiting technology based on a software algorithm. Comprising the following steps: converting an obtained original image from an RGB color space to an LCH color space; extracting brightness information of each pixel of the original image in an LCH color space and performing chromaticity compensation; acquiring and adjusting the color saturation compensation degree in real time according to the ambient light intensity; converting the brightness information of the compensated image from the LCH color space to the RGB color space to obtain a color image after color saturation compensation; embedding digital watermark information into the compensated image; carrying out compression processing on the image embedded with the digital watermark information; and decompressing the compressed image at a receiving end, and extracting the embedded digital watermark information to verify the authenticity of the image. According to the method, the anti-counterfeiting capability of the image is improved, and the image is protected on the premise that the image quality is not obviously influenced.

Description

Technical Field

[0001] The present invention relates to the technical field of image anti-counterfeiting, and in particular to an image anti-counterfeiting technology based on software algorithm. Background Art

[0002] With the rapid development of digital media technology, the use of images in the Internet and multimedia applications has become more and more extensive. However, this has also brought challenges to image copyright protection and anti-counterfeiting. Traditional image anti-counterfeiting technology mainly relies on physical marks or watermarks, but these methods are easily tampered with or removed during the reproduction and dissemination of digital images. Therefore, it is particularly important to develop an image anti-counterfeiting technology based on software algorithms, which can improve the anti-counterfeiting ability of images without significantly affecting the image quality.

[0003] At present, a variety of image processing technologies have been applied to the field of image anti-counterfeiting, such as digital watermarking technology, image encryption technology, and image feature extraction technology. Digital watermarking technology achieves copyright protection by embedding specific information in the image, but its anti-attack ability is limited and it is easily affected by image processing operations such as compression and filtering. Image encryption technology encrypts images through complex algorithms, but usually requires high computing resources and has security risks during image transmission and storage. Image feature extraction technology achieves anti-counterfeiting by extracting the feature information of the image, but it has a greater impact on image quality and the feature extraction process is complex. Summary of the invention

[0004] The image anti-counterfeiting technology based on software algorithm of the present invention not only improves the anti-counterfeiting ability of the image, but also realizes the protection of the image without significantly affecting the image quality.

[0005] The technical solution adopted by the present invention is: an image anti-counterfeiting technology based on software algorithm, comprising the following steps:

[0006] Step 1: Convert the acquired original image from RGB color space to LCH color space;

[0007] Step 2: Extract the brightness information of each pixel of the original image in the LCH color space, and map the brightness information to improve the overall contrast of the image;

[0008] Step 3: extract the brightness information of each pixel of the original image in the LCH color space for chromaticity compensation, including extracting the color saturation in the chromaticity information for color saturation compensation;

[0009] Step 4: Obtain and adjust the degree of color saturation compensation in real time according to the ambient light intensity;

[0010] Step 5: Convert the brightness information of the compensated image from the LCH color space back to the RGB color space to obtain a color image after color saturation compensation;

[0011] Step 6: embedding digital watermark information into the compensated image to improve the anti-counterfeiting ability of the image;

[0012] Step 7: compress the image embedded with the digital watermark information to meet different transmission and storage requirements;

[0013] Step 8: Decompress the compressed image at the receiving end and extract the embedded digital watermark information to verify the authenticity of the image.

[0014] As a further improvement of the present invention, the process of converting the original image from the RGB color space to the LCH color space specifically includes: the original image is first converted from the RGB color space to the Lab color space, and then converted from the Lab color space to the LCH color space.

[0015] As a further improvement of the present invention, the mapping processing of the brightness information includes: performing global nonlinear mapping on the brightness information to enhance the contrast of the low gray range of the image; dividing the brightness information of the image into a base layer and a detail layer, and performing global mapping processing on the base layer.

[0016] As a further improvement of the present invention, the chromaticity compensation specifically includes:

[0017] Degrading the original image to simulate the human eye's perception of the image quality displayed on the display screen under ambient light;

[0018] After the degraded image is converted from the RGB color space to the LCH color space, the color saturation of each pixel of the degraded image in the chromaticity information of the LCH color space is extracted;

[0019] The difference between the color saturation of each pixel of the original image and the degraded image is calculated, and the color saturation of each pixel of the compensated image after the chromaticity compensation is performed on the original image is calculated based on the difference.

[0020] As a further improvement of the present invention, the color saturation calculation formula of the compensated image is:

[0021] Cdiff=C-Cflare······(1)

[0022] C*=C+αCdiff······(2)

[0023] Where C represents the color saturation of the original image; Cflare represents the color saturation of the degraded image; Cdiff represents the difference between the two; α is the compensation coefficient and is less than or equal to 1, indicating the degree of color saturation compensation; C* represents the color saturation of the compensated image.

[0024] The larger the compensation coefficient is, the greater the degree of color saturation compensation is, and the brighter the color of the compensated image is.

[0025] As a further improvement of the present invention, the embedding process of the digital watermark information includes the following steps:

[0026] S1: Generate a pseudo-random sequence as digital watermark information. The sequence has good autocorrelation and low cross-correlation to ensure the concealment and robustness of the watermark.

[0027] S2: Convert the pseudo-random sequence into frequency domain representation using discrete Fourier transform (DFT) and discrete cosine transform (DCT) methods to embed the watermark in the frequency domain;

[0028] S3: Select the imperceptible area in the image for watermark embedding to reduce the impact on image quality;

[0029] S4: adjusting the strength of the watermark signal according to a predetermined embedding strategy to balance the invisibility and robustness of the watermark;

[0030] S5: embedding the adjusted watermark signal into the selected image area by modifying the pixel value and frequency domain coefficient of the image;

[0031] S6: After the watermark is embedded, an inverse transformation is performed to convert the image from the frequency domain back to the spatial domain to obtain the image embedded with the digital watermark information.

[0032] As a further improvement of the present invention, the compression process comprises the following steps:

[0033] Preprocess the image after embedding the digital watermark information to reduce the distortion that may be introduced in the compression process;

[0034] Select JPEG, JPEG2000, or PNG compression algorithms to compress images based on image content and transmission and storage requirements;

[0035] During the compression process, the compression parameters are optimized to ensure the best balance between image quality and compression rate;

[0036] After compression, the compressed image is evaluated to ensure that it meets the predetermined image quality standards.

[0037] As a further improvement of the present invention, the decompression process comprises the following steps:

[0038] According to the compression algorithm used to compress the image, a corresponding decompression algorithm is selected to perform a decompression operation to restore the image data;

[0039] Perform quality assessment on decompressed images to ensure that the images have not been unacceptably distorted during the compression process;

[0040] If it is detected that the image quality does not meet the standard, the decompression parameters are adjusted and the decompression operation is performed again until the image quality reaches the predetermined standard;

[0041] After confirming that the image quality is correct, the image is inversely transformed to convert the image from the compressed spatial domain back to the frequency domain;

[0042] Extract the digital watermark information embedded in the frequency domain and separate the watermark signal from the frequency domain coefficients through inverse transformation;

[0043] The extracted digital watermark information is verified to confirm the authenticity of the image.

[0044] Beneficial effects of the present invention:

[0045] (1) Improving image anti-counterfeiting capabilities: By embedding digital watermark information in images, images have higher anti-counterfeiting capabilities; the concealment and robustness of digital watermark information ensure that the authenticity of images can be accurately verified even after the images have been compressed, transmitted, and processed.

[0046] (2) Optimizing image quality: The present invention effectively improves the overall contrast and color saturation of the image by mapping the brightness information and performing chromaticity compensation in the LCH color space, making the image more vivid and clear visually while avoiding significantly affecting the image quality.

[0047] (3) Strong environmental adaptability: By acquiring the ambient light intensity in real time and adjusting the degree of color saturation compensation, the present invention can adapt to different viewing environments and ensure that the image can maintain good visual effects under various lighting conditions.

[0048] (4) High compression and transmission efficiency: The present invention optimizes compression parameters during the compression process to ensure the best balance between image quality and compression rate, so that the image has higher compression efficiency while meeting quality standards, which is convenient for transmission and storage.

[0049] (5) Easy authenticity verification: At the receiving end, the authenticity of the image can be easily verified by decompressing and extracting the digital watermark information, ensuring that the source of the image is reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1The present invention is a color saturation compensation diagram of an image anti-counterfeiting technology based on a software algorithm. DETAILED DESCRIPTION

[0051] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The present invention provides an image anti-counterfeiting technology based on a software algorithm, comprising the following steps:

[0053] Step 1: Convert the acquired original image from RGB color space to LCH color space;

[0054] Step 2: Extract the brightness information of each pixel of the original image in the LCH color space, and map the brightness information to improve the overall contrast of the image;

[0055] Step 3: extract the brightness information of each pixel of the original image in the LCH color space for chromaticity compensation, including extracting the color saturation in the chromaticity information for color saturation compensation;

[0056] Step 4: Obtain and adjust the degree of color saturation compensation in real time according to the ambient light intensity;

[0057] Step 5: Convert the brightness information of the compensated image from the LCH color space back to the RGB color space to obtain a color image after color saturation compensation;

[0058] Step 6: embedding digital watermark information into the compensated image to improve the anti-counterfeiting ability of the image;

[0059] Step 7: compress the image embedded with the digital watermark information to meet different transmission and storage requirements;

[0060] Step 8: Decompress the compressed image at the receiving end and extract the embedded digital watermark information to verify the authenticity of the image.

[0061] The process of converting the original image from the RGB color space to the LCH color space in the present invention specifically includes: the original image is first converted from the RGB color space to the Lab color space, and then converted from the Lab color space to the LCH color space.

[0062] The mapping process of the brightness information in the present invention includes: performing global nonlinear mapping on the brightness information to improve the contrast of the low-gray range of the image; dividing the brightness information of the image into a base layer and a detail layer, and performing global mapping on the base layer.

[0063] The chromaticity compensation described in the present invention specifically includes: degrading the original image to simulate the perception of the human eye on the quality of the image displayed on the display screen under ambient light; when the degraded image is converted from the RGB color space to the LCH color space, the color saturation of each pixel of the degraded image in the chromaticity information of the LCH color space is extracted; the difference between the color saturation of each pixel of the original image and the degraded image is calculated, and the color saturation of each pixel of the compensated image after the chromaticity compensation of the original image is calculated according to the difference.

[0064] The color saturation calculation formula of the compensated image in the present invention is:

[0065] Cdiff=C-Cflare······(1)

[0066] C*=C+αCdiff······(2)

[0067] Among them, C represents the color saturation of the original image; Cflare represents the color saturation of the degraded image; Cdiff represents the difference between the two; α is the compensation coefficient and is less than or equal to 1, indicating the degree of color saturation compensation; C* represents the color saturation of the compensated image. The larger the compensation coefficient, the greater the degree of color saturation compensation and the brighter the color of the compensated image.

[0068] The embedding process of the digital watermark information in the present invention comprises the following steps: S1, generating a pseudo-random sequence as the digital watermark information, the sequence having good autocorrelation and low cross-correlation to ensure the concealment and robustness of the watermark; S2, converting the pseudo-random sequence into a frequency domain representation, using discrete Fourier transform (DFT) and discrete cosine transform (DCT) methods to facilitate embedding the watermark in the frequency domain; S3, selecting an area in the image that is not easily perceived for watermark embedding to reduce the impact on the image quality; S4, adjusting the strength of the watermark signal according to a predetermined embedding strategy to balance the invisibility and robustness of the watermark; S5, embedding the adjusted watermark signal into the selected image area by modifying the pixel values ​​and frequency domain coefficients of the image; S6, after embedding the watermark, performing an inverse transform to convert the image from the frequency domain back to the spatial domain to obtain the image after the digital watermark information is embedded.

[0069] The compression process described in the present invention includes the following steps: (1) preprocessing the image after embedding the digital watermark information to reduce the distortion that may be introduced during the compression process; (2) selecting JPEG, JPEG2000, or PNG compression algorithms to perform compression according to the image content and transmission and storage requirements; (3) during the compression process, optimizing compression parameters to ensure the best balance between image quality and compression rate; (4) after the compression is completed, performing quality evaluation on the compressed image to ensure that it meets the predetermined image quality standard.

[0070] The decompression process described in the present invention includes the following steps: (1) selecting a corresponding decompression algorithm to perform a decompression operation based on the compression algorithm used to compress the image to restore the image data; (2) performing a quality assessment on the decompressed image to ensure that the image does not produce unacceptable distortion during the compression process; (3) if it is detected that the image quality does not meet the standard, adjusting the decompression parameters and re-performing the decompression operation until the image quality reaches the predetermined standard; (4) after confirming that the image quality is correct, performing an inverse transformation on the image to convert the image from the compressed spatial domain back to the frequency domain; (5) extracting the digital watermark information embedded in the frequency domain, and separating the watermark signal from the frequency domain coefficients through an inverse transformation; (6) verifying the extracted digital watermark information to confirm the authenticity of the image.

[0071] Example:

[0072] In order to better illustrate the specific implementation mode of the present invention, a specific example is provided below.

[0073] Assume that there is an original image that needs to be processed for anti-counterfeiting, and the image is a landscape photo with complex details and rich colors. First, the original image is converted from the RGB color space to the LCH color space for subsequent processing.

[0074] Step 1: Convert the original image from RGB color space to LCH color space. The specific operation is: first convert the image from RGB color space to Lab color space, and then convert Lab color space to LCH color space. During the conversion process, use the standard color space conversion formula to ensure the accuracy of the conversion.

[0075] Step 2: Extract the brightness information of each pixel in the original image in the LCH color space and perform mapping processing to improve the overall contrast of the image. The mapping process includes global nonlinear mapping of the brightness information to improve the contrast of the low-gray range of the image. The brightness information of the image is divided into a base layer and a detail layer, and the base layer is globally mapped to ensure that the details of the image are clear.

[0076] Step 3: Extract the brightness information of each pixel of the original image in the LCH color space for chromaticity compensation, including extracting the color saturation in the chromaticity information for color saturation compensation. Degrade the original image to simulate the human eye's perception of the image quality displayed on the display screen under different ambient light. After converting the degraded image from the RGB color space to the LCH color space, extract the color saturation of each pixel of the degraded image in the chromaticity information of the LCH color space. Calculate the difference in color saturation of each pixel of the original image and the degraded image, and calculate the color saturation of each pixel of the compensated image after chromaticity compensation of the original image based on the difference.

[0077] Step 4: Obtain and adjust the degree of color saturation compensation in real time according to the ambient light intensity. The ambient light intensity is monitored in real time by the ambient light sensor, and the compensation coefficient α is adjusted according to the ambient light intensity to ensure that the color performance of the image is always optimal under different lighting conditions.

[0078] Step 5: Convert the brightness information of the compensated image from the LCH color space back to the RGB color space to obtain a color image after color saturation compensation. During the conversion process, the same inverse formula as that used in the conversion to the LCH color space is used to ensure the accuracy of the conversion.

[0079] Step 6: Embed digital watermark information in the compensated image to improve the anti-counterfeiting ability of the image. Generate a pseudo-random sequence as the digital watermark information, which has good autocorrelation and low cross-correlation to ensure the concealment and robustness of the watermark. Convert the pseudo-random sequence into a frequency domain representation, and select an area in the image that is not easily perceived for watermark embedding to reduce the impact on image quality.

[0080] Step 7: Compress the image embedded with digital watermark information to meet different transmission and storage requirements. Select JPEG2000 compression algorithm for compression and optimize compression parameters to ensure the best balance between image quality and compression rate.

[0081] Step 8: Decompress the compressed image at the receiving end and extract the embedded digital watermark information to verify the authenticity of the image. According to the compression algorithm used to compress the image, select the corresponding decompression algorithm to perform the decompression operation to restore the image data. Perform a quality assessment on the decompressed image to ensure that the image does not produce unacceptable distortion during the compression process. Extract the digital watermark information embedded in the frequency domain and verify the extracted digital watermark information to confirm the authenticity of the image.

[0082] Through the above steps, the present invention provides an image anti-counterfeiting technology based on a software algorithm, which can effectively improve the anti-counterfeiting ability of the image and ensure that the color performance of the image under different ambient light conditions is always optimal.

[0083] In summary, the present invention provides an efficient and reliable image anti-counterfeiting technology, which not only improves the anti-counterfeiting ability of the image, but also realizes the protection of the image without significantly affecting the image quality. The technical solution has a wide range of application prospects and is suitable for various occasions requiring image anti-counterfeiting, such as digital media, e-commerce, copyright protection and other fields.

[0084] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An image anti-counterfeiting technology based on software algorithm, characterized in that: The following steps are involved: Step 1: Convert the acquired original image from RGB color space to LCH color space; Step 2: Extract the brightness information of each pixel in the original image in the LCH color space and perform mapping processing on the brightness information to improve the overall contrast of the image; Step 3: Extract the brightness information of each pixel in the original image in the LCH color space for chromaticity compensation, including extracting the color saturation in the chromaticity information for color saturation compensation; Step 4: Obtain and adjust the degree of color saturation compensation in real time according to the ambient light intensity; Step 5: Convert the brightness information of the compensated image from the LCH color space back to the RGB color space to obtain a color image after color saturation compensation; Step 6: Embed digital watermark information into the compensated image to improve the anti-counterfeiting ability of the image; Step 7: compress the image after embedding the digital watermark information to meet different transmission and storage requirements; Step 8: Decompress the compressed image at the receiving end and extract the embedded digital watermark information to verify the authenticity of the image.

2. The image anti-counterfeiting technology based on software algorithm according to claim 1, characterized in that: The process of converting the original image from the RGB color space to the LCH color space specifically includes: first converting the original image from the RGB color space to the Lab color space, and then converting from the Lab color space to the LCH color space.

3. The image anti-counterfeiting technology based on software algorithm according to claim 1, characterized in that: The mapping process of the brightness information includes: performing global nonlinear mapping on the brightness information to enhance the contrast of the low gray range of the image; dividing the brightness information of the image into a base layer and a detail layer, and performing global mapping on the base layer.

4. The image anti-counterfeiting technology based on software algorithm according to claim 1, characterized in that: The chromaticity compensation specifically includes: Degrade the original image to simulate the human eye's perception of the image quality displayed on the display screen under ambient light; After the degraded image is converted from the RGB color space to the LCH color space, the color saturation of each pixel of the degraded image in the chromaticity information of the LCH color space is extracted; The color saturation difference between each pixel of the original image and the degraded image is calculated, and the color saturation of each pixel of the compensated image after chromatic compensation of the original image is calculated based on the difference.

5. The image anti-counterfeiting technology based on software algorithm according to claim 1, characterized in that: The color saturation calculation formula of the compensated image is: Cdiff=C-Cflare······(1) C*=C+αCdiff······(2) Where C represents the color saturation of the original image; Cflare represents the color saturation of the degraded image; Cdiff represents the difference between the two; α is the compensation coefficient and is less than or equal to 1, indicating the degree of color saturation compensation; C* represents the color saturation of the compensated image. The larger the compensation coefficient is, the greater the degree of color saturation compensation is, and the brighter the color of the compensated image is.

6. The image anti-counterfeiting technology based on software algorithm according to claim 1, characterized in that: The embedding process of the digital watermark information includes the following steps: S1: Generate a pseudo-random sequence as digital watermark information. The sequence has good autocorrelation and low cross-correlation to ensure the concealment and robustness of the watermark. S2: Convert the pseudo-random sequence into frequency domain representation using discrete Fourier transform (DFT) and discrete cosine transform (DCT) methods to facilitate watermark embedding in the frequency domain; S3: Select the less noticeable area in the image for watermark embedding to reduce the impact on image quality; S4: Adjust the strength of the watermark signal according to the predetermined embedding strategy to balance the invisibility and robustness of the watermark; S5: embedding the adjusted watermark signal into the selected image area by modifying the pixel values ​​and frequency domain coefficients of the image; S6: After the watermark is embedded, an inverse transformation is performed to convert the image from the frequency domain back to the spatial domain to obtain the image embedded with the digital watermark information.

7. The image anti-counterfeiting technology based on software algorithm according to claim 1, characterized in that: The compression process comprises the following steps: Preprocess the image after embedding the digital watermark information to reduce the distortion that may be introduced during the compression process; Select JPEG, JPEG2000, or PNG compression algorithms based on image content and transmission and storage requirements; During the compression process, the compression parameters are optimized to ensure the best balance between image quality and compression rate; After compression is completed, the compressed image is evaluated to ensure that it meets the predetermined image quality standards.

8. The image anti-counterfeiting technology based on software algorithm according to claim 1, characterized in that: The decompression process comprises the following steps: According to the compression algorithm used to compress the image, a corresponding decompression algorithm is selected to perform a decompression operation to restore the image data; Perform quality assessment on the decompressed image to ensure that the image has not been unacceptably distorted during the compression process; If it is detected that the image quality does not meet the standard, the decompression parameters are adjusted and the decompression operation is performed again until the image quality meets the predetermined standard; After confirming that the image quality is correct, the image is inversely transformed to convert the image from the compressed spatial domain back to the frequency domain; Extract the digital watermark information embedded in the frequency domain and separate the watermark signal from the frequency domain coefficients through inverse transformation; The extracted digital watermark information is verified to confirm the authenticity of the image.

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