3D Image Security Processing System and Method Based on Diffusion Model

Abstract

The present invention relates to the field of image data processing, specifically a three-dimensional image security processing system and method based on a diffusion model. The system includes: an image format conversion module, an image feature training module, an image three-dimensional structure analysis module, an image light effect simulation module, an image rendering module, and an artificial intelligence security and image anti-counterfeiting module. Through the convolutional neural network, deep semantic analysis of image content can be achieved by processing a large amount of three-dimensional image data, enabling the model to not only identify the key structural elements in the image but also predict the positions and mutual relationships of these elements in three-dimensional space, thereby significantly improving the recognition accuracy and application efficiency of the model. Combining the ray tracing technology and the neural renderer of the neural network, the physical propagation process of light is simulated, enhancing the realism of the rendering process. At the same time, the accuracy, authenticity, and security of image processing are effectively integrated, comprehensively improving the reliability and performance in image generation and use.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a three-dimensional image security processing system and method based on a diffusion model. Background Art

[0002] 3D processing image generation systems involve using three-dimensional image processing techniques to create highly realistic three-dimensional visual effects. It is usually applied in fields such as film production, video games, virtual reality, and simulation training. In addition, the purpose of 3D image generation is to provide a visual experience that is almost indistinguishable from reality, enabling users to interact more naturally and deeply in a virtual environment.

[0003] Traditional three-dimensional image processing based on image rendering or neural rendering methods is difficult to achieve satisfactory rendering effects in a large view when reconstructing fine geometries. Secondly, since the information provided by a single perspective is limited, inferring 3D information from a single image is a complex and error-prone process. In addition, many existing 3D generation networks are mainly modeled for specific categories of objects and perform poorly when generalized to general or uncommon 3D objects. Moreover, even when using advanced techniques such as Neural Radiance Fields (NeRF), there are still biases in the fidelity of textures and colors, making the generated 3D models look too smooth or have inaccurate color saturation. These defects emphasize the need for more innovative methods to improve the conversion efficiency and quality from a single image to high-fidelity 3D content. In terms of security and anti-counterfeiting, it is easy to lead to the risk of information leakage or tampering in the generated images, or there may be harmful information, unable to guarantee the security and credibility of the image content. Summary of the Invention

[0004] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a three-dimensional image security processing system and method based on a diffusion model.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, a three-dimensional image security processing system based on a diffusion model is provided. The system includes:

[0007] An image format conversion module for performing coordinate alignment, resolution calibration, and format conversion on the image data obtained by three-dimensional scanning to generate normalized three-dimensional data;

[0008] An image feature training module for constructing a deep learning model based on the diffusion model and the normalized three-dimensional data, and training the image data to learn the feature and structure information of the image to obtain a trained deep model;

[0009] The 3D structure analysis module of the image is used to utilize the trained deep model to analyze the shape, size, and relative position of the 3D object in the image, reconstruct the 3D space, and obtain 3D space image data;

[0010] The image light effect simulation module is used to simulate the influence of the light source on the image based on the 3D space image data, calculate the light scattering, reflection, and shadow effects after the interaction between the light and the 3D object, and obtain the light simulation data;

[0011] The image rendering module is used to adjust the rendering parameters based on the light simulation data, perform image rendering processing, and output the optimized rendered image;

[0012] The artificial intelligence security and image anti-counterfeiting module is used to perform security analysis and content review on the optimized rendered image using artificial intelligence, and generate anti-counterfeiting image data by embedding digital watermarks and performing image content monitoring.

[0013] In one implementation, the image format conversion module includes:

[0014] The coordinate alignment sub-module is used to perform consistency check and alignment of the spatial coordinates based on the image data obtained by 3D scanning, and generate the aligned image data;

[0015] The resolution calibration sub-module is used to detect and adjust the image pixel resolution of the aligned image data, and generate the calibrated image data;

[0016] The data formatting sub-module is based on the calibrated image data, performs data format conversion operations, converts the image data into a unified point cloud format, and generates normalized 3D data.

[0017] In one implementation, the image feature training module includes:

[0018] The model construction sub-module is used to select the network layer structure and activation function based on the normalized 3D data, define the input and output nodes and the number of network layers, and generate the initialized deep learning model framework;

[0019] The feature learning sub-module is used to adopt the initialized deep learning model framework, input 3D image data for feature extraction, analyze the structural information and texture features of the image, optimize the model parameters, and generate the feature optimization model;

[0020] The model training sub-module performs multiple rounds of training on the basis of optimizing the model with the features, adjusts the learning rate and loss function, and uses the batch training method to optimize the generalization ability of the model, generating a trained deep model. Among them, in each round of training, the diffusion model is used to calculate the loss according to the difference between the current noise prediction and the actual noise, so as to guide the model to learn how to process the noise terms at different time steps, where the noise terms are used to reflect the noise accumulation effect based on the time steps.

[0021] In one implementation, the image three-dimensional structure analysis module includes:

[0022] The image shape analysis sub-module is used to draw the contour of the three-dimensional object based on the trained deep model, perform shape analysis by comparing the drawn contour lines with the image edges, record the shape features, and generate image shape data;

[0023] The image size analysis sub-module is used to measure the size of the three-dimensional object based on the image shape data. By measuring the height, width, and depth of each three-dimensional object and comparing with the standard size reference in the three-dimensional model, the size data is statistically summarized to obtain the image size data;

[0024] The image position analysis sub-module is used to locate the position of the three-dimensional object in space through the image size data. According to the spatial relationship and depth information between the three-dimensional objects, the coordinate information of each three-dimensional object is recorded, and the data is updated in the three-dimensional model to obtain the three-dimensional space image data.

[0025] In one implementation, the image light effect simulation module includes:

[0026] The light source setting sub-module is used to configure the light source based on the three-dimensional space image data through the BRDF model to simulate the effect of natural light or indoor lighting, adjust the light source position and intensity, and set multiple lighting angles and color temperatures to generate light source configuration data. The BRDF model is a bidirectional reflectance distribution function model;

[0027] The light interaction sub-module is used to analyze the interaction between the light and the surface of the three-dimensional object by using the light source configuration data, calculate the reflection and scattering effects, adjust the light interaction behavior according to the material and surface characteristics of the object, and simulate the performance of the light in various environments to generate light environment interaction data;

[0028] The shadow generation sub-module is used to locate the shadow area generated by the block between the light source and the object through the light environment interaction data, calculate the intensity and extension of the shadow, adjust the blurriness and boundary of the shadow, optimize the light and dark contrast in the image, and obtain the light simulation data.

[0029] In one embodiment, the BRDF model follows the formula:

[0030]

[0031] to calculate the relationship between incident light and reflected light when incident from multiple angles, and simulate the effect of natural light or indoor lighting;

[0032] where the function represents the bidirectional reflectance distribution function, and is used to calculate the light reflectance of a given material surface in the incident light direction and the reflected light direction ; is the incident light direction, is the reflected light direction, is the incident angle, is the viewing angle, is the material property, is the diffuse reflection coefficient, representing the proportion of the diffuse reflection part of the material surface, is the cosine value of the angle, used to calculate the effective component of the light incident on the surface, is the specular reflection coefficient, used to represent the proportion of the specular reflection part of the material surface, is the specular highlight attenuation factor, which adjusts the intensity of the specular reflection according to the viewing angle so that the highlight effect weakens as the viewing angle increases, is the light radiation intensity in the unit reflection direction, characterizing the light brightness per unit area in the reflection direction ; is the light intensity in the unit incident direction, characterizing the light energy received per unit area in the incident direction ;

[0033] In one embodiment, the image rendering module includes:

[0034] A parameter adjustment sub-module, which is used to analyze the light intensity and color distribution in the image based on the ray simulation data, adjust the parameters one by one to match the predetermined visual effect, and adjust the color saturation and brightness level to generate image parameter optimization data;

[0035] A detail enhancement sub-module, which is used to adopt the image parameter optimization data to perform hierarchical separation, contrast and sharpness adjustment on the details in the image, and optimize the key visual elements in the image, including texture and edges, to obtain an image with optimized details;

[0036] An image output sub-module, which is used to perform color correction and resolution adjustment on the image with optimized details, and output the optimized rendered image.

[0037] Based on the same inventive concept, the second aspect of the present invention provides a three-dimensional image security processing method based on a diffusion model, which is implemented based on the three-dimensional image security processing system described in the first aspect. The method includes the following steps:

[0038] S1: Perform coordinate alignment, resolution calibration, and format conversion on the image data obtained by three-dimensional scanning to generate normalized three-dimensional data;

[0039] S2: Construct a deep learning model based on the diffusion model and the normalized three-dimensional data, and train the image data to learn the feature and structural information of the image to obtain a trained deep model;

[0040] S3: Use the trained deep model to analyze the shape, size, and relative position of the three-dimensional objects in the image, reconstruct the three-dimensional space, and obtain three-dimensional space image data;

[0041] S4: Based on the three-dimensional space image data, simulate the influence of the light source on the image, calculate the light scattering, reflection, and shadow effects after the interaction of light with the three-dimensional objects to obtain light simulation data;

[0042] S5: Based on the light simulation data, adjust the rendering parameters, perform image rendering processing, and output an optimized rendered image;

[0043] S6: Use artificial intelligence to perform security analysis and content review on the optimized rendered image, and generate anti-counterfeiting image data by embedding digital watermarks and performing image content monitoring.

[0044] Based on the same inventive concept, the third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the three-dimensional image security processing method based on the diffusion model described in the second aspect.

[0045] Based on the same inventive concept, the fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the three-dimensional image security processing method based on the diffusion model described in the second aspect.

[0046] Compared with the prior art, the advantages and beneficial technical effects of the present invention are as follows:

[0047] The system or method proposed by the present invention constructs a deep model based on a convolutional neural network. Through the convolutional neural network, it can achieve deep semantic parsing of image content by processing a large amount of three-dimensional image data, enabling the model to not only identify key structural elements in the image but also predict the positions and mutual relationships of these elements in three-dimensional space, thereby significantly improving the recognition accuracy and application efficiency of the model. Combining a ray tracing technology and a neural renderer of a neural network, by simulating the physical propagation process of light, it enhances the realism of the rendering process from a single image for creating high-fidelity three-dimensional content. Using a diffusion prior as 3D perception supervision, the generated 3D model shows a faithful geometric shape and realistic texture with a diffusion clip loss and texture point cloud enhancement. Making 3D applicable to general objects endows multi-purpose applications. It provides users with a brand-new 3D creation experience. Through artificial intelligence technology, digital watermarks are embedded and image monitoring is carried out to ensure the security and traceability of image content. It effectively integrates the accuracy, realism, and security of image processing, comprehensively improving the reliability and performance in image generation and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a module diagram of a three-dimensional image security processing system based on a diffusion model proposed in an embodiment of the present invention;

[0050] Figure 2 It is a framework diagram of a three-dimensional image security processing system based on a diffusion model proposed in an embodiment of the present invention;

[0051] Figure 3 It is a schematic diagram of the processing flow of an image format conversion module in a three-dimensional image security processing system based on a diffusion model proposed in an embodiment of the present invention;

[0052] Figure 4 It is a schematic diagram of the processing flow of an image feature training module in a three-dimensional image security processing system based on a diffusion model proposed in an embodiment of the present invention;

[0053] Figure 5 It is a schematic diagram of the processing flow of an image three-dimensional structure parsing module in a three-dimensional image security processing system based on a diffusion model proposed in an embodiment of the present invention;

[0054] Figure 6Schematic diagram of the processing flow of the image light effect simulation module in the three-dimensional image security processing system based on the diffusion model proposed in the embodiment of the present invention;

[0055] Figure 7 Schematic diagram of the processing flow of the image rendering module in the three-dimensional image security processing system based on the diffusion model proposed in the embodiment of the present invention;

[0056] Figure 8 Schematic diagram of the processing flow of the artificial intelligence security and image anti-counterfeiting module in the three-dimensional image security processing system based on the diffusion model proposed in the embodiment of the present invention. Detailed implementation manners

[0057] A three-dimensional image security processing system based on the diffusion model proposed by the present invention includes: an image format conversion module that performs coordinate alignment and resolution calibration on the image data obtained by three-dimensional scanning, converts the image into a VOX voxel or point cloud format, and generates normalized three-dimensional data. In the present invention, through a convolutional neural network, deep semantic parsing of image content can be achieved by processing a large amount of three-dimensional image data, enabling the model to not only identify key structural elements in the image, but also predict the positions and mutual relationships of these elements in three-dimensional space, thereby significantly improving the recognition accuracy and application efficiency of the model. Combining a neural renderer that combines ray tracing technology and a neural network, by simulating the physical propagation process of light, the realism of the rendering process is enhanced. At the same time, the accuracy, realism, and security of image processing are effectively integrated, comprehensively improving the reliability and performance in image generation and use.

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1

[0060] Please refer to Figure 1 , the present invention provides a three-dimensional image security processing system based on the diffusion model, and the system includes:

[0061] An image format conversion module, configured to perform coordinate alignment, resolution calibration, and format conversion on the image data obtained by three-dimensional scanning, and generate normalized three-dimensional data;

[0062] An image feature training module, configured to construct a deep learning model based on the diffusion model and the normalized three-dimensional data, and train the image data to learn the feature and structure information of the image, and obtain the trained deep model;

[0063] An image three-dimensional structure analysis module, which is used to utilize the trained deep model to analyze the shape, size and relative position of three-dimensional objects in the image, reconstruct the three-dimensional space, and obtain three-dimensional space image data;

[0064] An image light effect simulation module, which is used to simulate the influence of a light source on the image based on the three-dimensional space image data, calculate the light scattering, reflection and shadow effects after the interaction between the light and the three-dimensional object, and obtain light simulation data;

[0065] An image rendering module, which is used to adjust the rendering parameters based on the light simulation data, perform image rendering processing, and output an optimized rendered image;

[0066] An artificial intelligence security and image anti-counterfeiting module, which is used to perform security analysis and content review through artificial intelligence, and ensure that the image does not contain harmful information and its source can be traced by embedding digital watermarks and performing image content monitoring, and generate anti-counterfeiting image data.

[0067] Specifically, the normalized three-dimensional data includes the spatial resolution of voxel data, the gray level of voxels, and the point cloud density parameter. The trained deep model includes the weight distribution of the model, the type of activation function, and the number of training epochs. The three-dimensional space image data includes the reconstructed three-dimensional grid density, the topological structure of the grid, and the spatial coordinates of key feature points. The light simulation data includes the light source position parameter, the light intensity level, and the angular distribution of reflected light and scattered light. The optimized rendered image includes the resolution of the image, the dynamic range, and the color correction parameter.

[0068] Please refer to Figure 2 and Figure 3 for the processing flow of the image format conversion module including:

[0069] The coordinate alignment sub-module performs consistency check and alignment of spatial coordinates based on the image data obtained by three-dimensional scanning. The specific process of generating the aligned image data is as follows:

[0070] The coordinate alignment sub-module is based on the image data obtained by three-dimensional scanning. First, by analyzing the position information of multiple key points in the three-dimensional space, key feature points are extracted and compared with the reference points in the standard model library. The position of each point is optimized by the least squares method to ensure the accurate alignment of each point in the three-dimensional space. During the alignment process, the deviation between each data point and the reference point is calculated, and the points beyond the error range are corrected or removed to ensure that the generated aligned image data has high accuracy and consistency. In addition, the alignment sub-module maintains an error log to record the accuracy and correction measures of each alignment operation for subsequent analysis and optimization, and generates the aligned image data.

[0071] The resolution calibration sub-module uses the aligned image data to detect and adjust the pixel resolution of the image. In the specific implementation process, the specific process of generating the calibrated image data is as follows:

[0072] The resolution calibration sub-module uses the aligned image data to analyze each pixel of the image, calculates the color and gray-level differences between it and the surrounding pixels to determine whether its resolution needs to be adjusted. Using a set of predefined standard resolution profiles, the actual resolution of the image pixels is compared with these standards, and the closest configuration is selected for adjustment. The image resolution is adjusted through interpolation or downsampling techniques to meet the requirements of subsequent processing. The calibration process is repeated until the average resolution error of the image is minimized. After calibration, the resolution comparison data of each pixel before and after adjustment is output to generate the calibrated image data.

[0073] Based on the calibrated image data, the data formatting sub-module performs data format conversion operations to convert the image data into a unified point cloud format. In the specific implementation process, the specific process of generating the standardized three-dimensional data is as follows:

[0074] Based on the calibrated image data, the data formatting sub-module first evaluates the structural density and spatial distribution of the data, and selects the VOX voxel or point cloud format according to the requirements. For the case of selecting VOX voxels, the module will calculate the spatial position and corresponding gray level of each voxel, and map the two-dimensional pixels to the three-dimensional voxels through volume rendering technology. For the point cloud format, the module converts the position and color information of each pixel into a point in the point cloud, retaining its three-dimensional coordinates and color attributes. During the conversion process, the data will be compressed to optimize the storage and processing speed, and finally generate the standardized three-dimensional data suitable for subsequent depth processing and analysis.

[0075] Please refer to Figure 2 and Figure 4 , the processing flow of the image feature training module includes:

[0076] Based on the standardized three-dimensional data, the model construction sub-module selects the network layer structure and activation function, defines the input and output nodes and the number of network layers. The specific process of generating the initial deep learning model framework is as follows:

[0077] The model construction sub-module generates an initial deep learning model framework based on the normalized 3D data. This process includes data feature analysis, network structure selection, and network parameter initialization. First, by statistically analyzing the feature density and distribution uniformity in the 3D data, a suitable network layer structure is selected. For example, it is determined how many convolutional layers to use and the number of filters in each layer. For the activation function, ReLU is selected according to the data characteristics to increase the ability of computational non-linear processing, or Sigmoid to achieve the probability distribution of the output layer. The input and output nodes are determined by the number of voxels in the 3D data and the number of expected classification categories. The depth and width of the network are set to achieve the expected recognition accuracy and processing speed, generating an initial deep learning model framework. The diffusion model calculates the loss in each iteration based on the difference between the current noise prediction and the actual noise, thereby guiding the model to learn how to better handle the noise terms at different time steps. As the training progresses, the NeRF parameters are updated, during which the 3D object gradually shows its texture and geometry.

[0078] The feature learning sub-module adopts the initial deep learning model framework, inputs the 3D image data for feature extraction, analyzes the structural information and texture features of the image, and optimizes the model parameters. The specific process of generating the feature-optimized model is as follows:

[0079] The feature learning sub-module adopts the initial deep learning model framework and inputs the processed 3D image data to extract features layer by layer through the network layers. First, the convolutional layer extracts the local features of the image through filters, and then the pooling layer reduces the spatial dimension of the features and reduces the computational amount. At this stage, each convolutional layer may be followed by a normalization layer to stabilize the learning process. By iteratively adjusting the network parameters, the model parameters including weights and biases are optimized. During this process, the loss function is used to evaluate the difference between the prediction result and the actual label. Usually, the mean squared error (MSE) is used as the loss function, and the expression of the loss function is: , where is the number of samples, is the actual value, is the predicted value. The loss is optimized by the gradient descent method to improve the fitting accuracy of the model to the data, generating a feature-optimized model.

[0080] The model training sub-module is based on the feature-optimized model, conducts multiple rounds of training, adjusts the learning rate and the loss function, and uses the batch training method to optimize the generalization ability of the model. The specific process of generating the trained deep model is as follows:

[0081] The model training sub-module sets multiple rounds of training based on the feature optimization model to adapt to the learning needs at different stages. The learning rate and loss function are adjusted to improve the model training process and enhance the generalization ability. For example, a relatively high learning rate may be adopted initially for rapid convergence, and the learning rate is gradually decreased as the training progresses to refine the learning effect. The batch training method is used, that is, a batch of data is used in each iteration. This can reduce memory usage and at the same time utilize the statistical characteristics of the batch data to stabilize the gradient estimation. In addition, to prevent overfitting, the Dropout technique may be introduced to randomly turn off some neurons in the network during each training iteration. Through repeated training, the network structure and parameters are adjusted, and finally a trained deep model is generated, which can effectively process and interpret the complex structure of 3D image data.

[0082] Before the formal training and interpretation, relevant preparatory work is required. A text-to-image diffusion model can be used to guide the optimization of the 3D representation. Let be the rendered image for a given viewpoint where G is the differentiable rendering function of the parameterized 3D representation. Specifically, the image x rendered by the diffusion model introduces a random amount of noise , at different time steps t, that is , where, represents the noise term introduced during the rendering process, represents the rendered image, which is usually used to describe an image representation with noise perturbations, reflecting the cumulative effect of noise based on the time step t. is the decay coefficient or noise scaling coefficient in the diffusion model, which is used to control the change of noise with the time step t during the image generation process. Usually, as the time step t increases, the decay coefficient gradually decreases to balance the influence of noise and the original image data. is the standard deviation of the noise in the diffusion model, which is used to describe the intensity of the noise added at the t-th time step. It determines the magnitude of the noise term at the current time step. As t increases, the standard deviation usually becomes larger. is the introduced noise term, which is used to simulate the random perturbations added during the rendering process. By adding different noises, diverse image samples can be generated. ; and define a noise table, whose log signal-to-noise ratio , which decreases linearly with the time step t. To optimize the 3D representation parameters and make the image as close as possible to a better generated sample, a fractional distillation loss L pushes the rendered image to a higher density region based on the text embedding. Specifically, L is the sign of the loss function, which in such an image generation model or diffusion model is used to measure the difference between the noise predicted by the model and the actually added noise. L calculates the difference between the predicted noise and the added noise according to the pixel gradient, and is used to update the scene parameters. It can be proved that this loss essentially measures the similarity between the image and the text prompt. The loss function L in the diffusion model serves as the main objective function during the training process. The diffusion model calculates the loss based on the difference between the current noise prediction and the actual noise at each iteration step, thereby guiding the model to learn how to better handle the noise term at different time steps. As the training progresses, the NeRF parameters are updated, during which the 3D object gradually reveals its texture and geometry.

[0083] Please refer to Figure 2 and Figure 5 , the processing flow of the image 3D structure analysis module includes:

[0084] Based on the trained deep model, the image shape analysis sub-module draws the contours of 3D objects, conducts shape analysis by aligning the drawn contour lines with the image edges, records the shape features, and generates image shape data. In the specific implementation process, the specific flow of generating image shape data is as follows;

[0085] Based on the trained deep model, the image shape analysis sub-module first performs high-precision edge detection on the 3D scanned image, and precisely draws the contours of each 3D object using an edge detection algorithm. This algorithm is based on the gradient information of the image, calculates the gradient intensity and direction of each pixel point, and extracts clear edge lines. Subsequently, the module compares these contour lines with the actual edges of the image, uses the Euclidean distance to quantify the deviation between the contour lines and the actual edges, and adjusts the model parameters to minimize this deviation. During this process, shape features such as angles and curvatures are detailedly recorded for further shape analysis. This ensures a high degree of consistency between the drawn contour lines and the actual 3D objects, and generates image shape data.

[0086] The image size analysis sub-module uses the image shape data to measure the dimensions of 3D objects. By measuring the height, width, and depth of each object, comparing with the standard size reference in the 3D model, statistically summarizing the size data, and obtaining the image size data. The specific flow is as follows:

[0087] The image size analysis sub-module uses the image shape data to measure the dimensions of each three-dimensional object in detail. First, the measurement reference points of each object are defined, and then the distances between these reference points are calculated to determine the height, width, and depth of the object. The measurement uses the straight-line distance formula in three-dimensional space: Distance = , where (x1, y1, z1) and (x2, y2, z2) are the three-dimensional coordinates of the reference points. By comparing these measured dimensions with the standard dimension references in the three-dimensional model, the accuracy of the dimensions is verified and adjusted. All dimension data are statistically summarized to form complete image dimension data, providing a basis for subsequent model verification and adjustment.

[0088] The image position analysis sub-module locates the position of the three-dimensional object in space through the image dimension data. According to the spatial relationship and depth information between objects, the coordinate information of each object is recorded, and the data are updated in the three-dimensional model. The specific process of obtaining the three-dimensional space image data is as follows:

[0089] The image position analysis sub-module accurately locates the position of the three-dimensional object in space through the image dimension data. Vector calculation is used to describe the spatial position of each object, and its vector coordinates relative to the model origin are calculated. The specific calculation method involves the vector representation from the reference point of each three-dimensional object to the model origin. The vector representation is v = (x, y, z), where x, y, and z represent the coordinates of the point in three-dimensional space respectively. This vector representation provides the accurate position information of each object in three-dimensional space, enabling the module to accurately locate each object and analyze the relative relationship between them. The position information of each object and its spatial relationship with other objects are detailedly recorded, and these data are updated in the three-dimensional model using a specific update algorithm. These position data provide important spatial geometric information for further analysis and optimization of the model, and finally form detailed three-dimensional space image data.

[0090] Please refer to Figure 2 and Figure 6 , the processing flow of the image light effect simulation module includes:

[0091] The light source setting sub-module, based on the three-dimensional space image data, configures the light source through the BRDF model to simulate the effects of natural light or indoor lighting, adjusts the light source position and intensity, and sets multiple lighting angles and color temperatures to generate light source configuration data. The specific process is as follows;

[0092] Based on the three-dimensional spatial image data, the light source setting sub-module first uses the BRDF (Bidirectional Reflectance Distribution Function) model to simulate the effects of different types of light sources, such as natural light and indoor lighting. According to the requirements of the model, the position of the light source is adjusted to match the actual environment of the scene, such as the position of the skylight or the indoor lighting fixtures. In addition, the intensity of the light source is adjusted to conform to the changes of natural light at different times or the requirements of indoor lighting. By changing the lighting angle and color temperature, such as the sunset color temperature of sunlight or the warm tone of indoor lights, the light effect is further refined. The precise setting of these parameters ensures that the simulated light can realistically reflect the lighting conditions in the real world and generate detailed light source configuration data.

[0093] The BRDF model calculates the relationship between the incident light and the reflected light when incident from multiple angles according to the formula:

[0094]

[0095] to simulate the effects of natural light or indoor lighting;

[0096] where the function represents the bidirectional reflectance distribution function, which is used to calculate the light reflectance of a given material surface in a specific incident light direction and the reflected light direction . is the incident light direction, indicating the direction in which the light enters the object surface. is the reflected light direction, indicating the direction in which the light is reflected from the object surface. is the incident angle, that is, the angle between the incident light and the normal of the object surface. is the viewing angle, that is, the angle between the reflected light seen by the observer and the normal of the object surface. is the material property, representing the physical and optical properties of the surface irradiated by the light. is the diffuse reflection coefficient, which is used to represent the proportion of the diffuse reflection part of the material surface. is the cosine value of the angle, which is used to calculate the effective component of the light incident on the surface. is the specular reflection coefficient, which is used to represent the proportion of the specular reflection part of the material surface. is the specular highlight attenuation factor, which adjusts the intensity of the specular reflection according to the viewing angle so that the highlight effect weakens as the viewing angle increases. is the radiant intensity of light in the unit reflection direction, that is, the light brightness per unit area in the reflection direction . is the illumination intensity in the unit incident direction, that is, the light energy received per unit area in the incident direction .

[0097] In the specific implementation process, the execution process is as follows:

[0098] Determine the angle parameter and :

[0099] (The angle of incidence) is determined by the angle between the incident light ray and the normal vector of the object surface. In actual calculations, it can be obtained by the arccosine of the dot product of the light source direction vector and the surface normal vector: where is the unit normal vector, is the unit incident light ray vector.

[0100] (The viewing angle) is determined by the angle between the reflected light direction and the observer direction. Similar to , it can be obtained by the arccosine of the dot product of the reflected light direction vector and the viewing direction vector: where is the reflection vector, is the viewing vector.

[0101] Diffuse reflection coefficient and specular reflection coefficient Determination:

[0102] These coefficients are usually preset according to the physical and optical properties of the material and can be obtained from the material database or determined by experimental measurement. It reflects the ability of different materials to absorb and reflect light.

[0103] Light radiation intensity and illumination intensity Calculation:

[0104] is the light radiation intensity reflected from the unit area of the material surface in the given reflection direction . This is usually obtained by simulating in rendering software through the ray tracing algorithm. is the illumination intensity received by the unit area of the object surface in the given incident direction , and is also obtained by ray tracing or actual illumination environment measurement.

[0105] Formula integration calculation:

[0106] The final BRDF value is calculated by inserting the parameter values obtained from all these calculations into the formula. In order to comprehensively consider the influence of material properties, light angles, and light intensities on reflection characteristics.

[0107] The calculated result Used to accurately simulate and render the visual appearance of objects in a 3D scene under different lighting conditions. This includes how light is absorbed, scattered, and reflected by the surface of an object, enabling the generation of realistic images for use in film production, video games, virtual reality, and any application that requires precise lighting and material representation.

[0108] The light interaction sub-module uses light source configuration data to analyze the interaction between light and the surface of 3D objects, calculates reflection and scattering effects, adjusts the light interaction behavior according to the material and surface characteristics of the object, simulates the performance of light in various environments, and the specific process of generating light environment interaction data is as follows:

[0109] The light interaction sub-module uses light source configuration data to analyze the interaction between light and the surface of 3D objects. This module first uses the bidirectional reflectance distribution function (BRDF) model to calculate the reflection and scattering effects of light emitted from the light source when it encounters the surface of 3D objects. Specifically, the module considers the incident angle θ and the reflection angle (i.e., the viewing angle). The formula for calculating the reflection intensity is: Reflection intensity = Light source intensity × BRDF(θ, ) × cos(θ), where the light source intensity represents the initial intensity of the light emitted by the light source, BRDF(θ, ) is the reflection coefficient, and cos(θ) represents the influence of the incident angle on the light intensity. The scattering effect depends on the roughness and scattering coefficient of the object surface, and its calculation formula is: Scattered light intensity = Light source intensity × Scattering coefficient × (1 / 2π), where the scattering coefficient describes the scattering ability of the object surface, and (1 / 2π) is the normalization coefficient for omnidirectional scattering. Through these calculations, the module simulates the performance of light in various environments, and the generated light environment interaction data not only accurately reflects the physical interaction between light and objects but also adapts to the requirements of different visual scenes.

[0110] The shadow generation sub-module uses the light environment interaction data to locate the shadow areas generated by the obstruction between the light source and the object, calculates the intensity and extension of the shadows, adjusts the blur and boundaries of the shadows, optimizes the light-dark contrast in the image, and the specific process of obtaining the light simulation data is as follows:

[0111] The shadow generation sub-module uses the light environment interaction data to locate the shadow areas generated by the obstruction between the light source and the object. It uses ray tracing technology to calculate the straight-line paths from the light source to various parts of the model, detects whether these paths are blocked by other objects, and thus determines the formation areas of the shadows. The intensity and extension of the shadows are calculated based on the distance and angle of the light source. The "shadow attenuation factor" is used to adjust the blur of the shadow edges, and this factor is adjusted according to a function of the distance from the light source to the shadow area. The specific calculation formula is: , where k is the attenuation coefficient and d is the distance from the light source to the shadow generation point. Thus, the light and dark contrast in the image is optimized, and accurate light simulation data is finally obtained.

[0112] Please refer to Figure 2 and Figure 7 , the image rendering module includes:

[0113] Based on the light simulation data, the parameter adjustment sub-module analyzes the light intensity and color distribution in the image, adjusts the parameters one by one to match the predetermined visual effect, and adjusts the color saturation and brightness level. The specific process of generating the optimized image parameter data is as follows:

[0114] Based on the light simulation data, the parameter adjustment sub-module first conducts a detailed analysis of the light intensity and color distribution in the image. This process involves evaluating the overall brightness of the image and the intensity distribution of each color channel. By using these data, the color saturation and brightness level of the image are adjusted. L essentially measures the similarity between the image and the given text prompt. And L can generate a 3D model corresponding to the text prompt, but they cannot be fully aligned with the reference image because the text prompt cannot capture all the object details. Through a diffusion CLIP loss, denoted as , which additionally forces the generated model to match the reference image: ; here is a CLIP image encoder, rather than directly measuring the CLIP loss on X , encoding the new view rendering , and then denoising it into a clean image , where X represents the image or data sample generated by the model. By applying a similarity loss to the denoised image sampled from the diffusion model, rendering is encouraged to align with the reference image while resembling high-quality samples from frozen diffusion. Incorporating the diffusion prior ensures that the generated 3D model is visually appealing and credible while also conforming to the given image. Nevertheless, even though the rendered image may seem meaningful to the diffusion model, there are still shape ambiguities, leading to problems such as sunken faces, over-planar geometry, or depth blurring. These issues are mitigated by leveraging the depth learned previously from a rich set of external images and performing supervision directly in 3D. Specifically, an off-the-shelf single-view depth estimator is used to estimate the depth d of the input image. Although the estimated depth may not accurately describe the geometric details, it is sufficient to ensure reasonable geometry and resolve most of the ambiguities. To account for inaccuracies and scale mismatches in d, the negative Pearson correlation between the estimated depth and depth d is regularized, modeled by NeRF at the reference viewpoint, schematic diagram of texture point cloud construction. The goal is to establish dense points and texture visible points using the reference image, and invisible points from NeRF. Through this regularization, NeRF depth estimation is encouraged to be linearly correlated with the depth prior. Multiple loss functions are used during model training, which are used for different objectives and optimization processes. By combining these loss functions, the model can be optimized in terms of overall performance, similarity to the reference image, stability of the diffusion process, and consistency of depth information, thereby improving the quality of the generated image and ensuring the stability of the model during the optimization process. Here, a progressive training strategy is adopted, starting from a narrow view range near the reference view and gradually expanding the range during training. Through progressive training, it is possible to achieve a 360-degree reconstruction of an object.

[0115] The detail enhancement sub-module uses image parameter-optimized data to hierarchically separate the details in the image, adjust the contrast and sharpness, and optimize the key visual elements in the image, including textures and edges. The specific process of obtaining the detail-optimized image is as follows:

[0116] The detail enhancement sub-module constructs a point cloud According to and of NeRF, the existing points , where are the extrinsic and intrinsic matrices of the camera, P represents point-to-point projection, represents the rendered depth, represents the alpha mask rendered according to the reference view , M is a mask generation function, and the input is the parameters of the reference view used to distinguish different regions in the image (such as foreground and background), thereby providing a basis for subsequent optimization and adjustment of the image. Indicates a point cloud generated based on a reference view. During image or 3D rendering, a point cloud is a collection of points in 3D space in the image, used to describe depth information and geometric shapes in 3D space. These points are visible in the reference view and are thus colored with the ground truth texture. For projections onto the remaining views, a new view is used. Importantly, it is necessary to avoid introducing points that overlap with existing points but have conflicting colors. To this end, the existing points are projected onto the new view to generate a mask indicating the presence of existing points. Using this as a guide, only points that have not yet been observed , which have not been observed yet, are lifted. Then, NeRF rendering is performed and integrated into the dense point cloud, indicating the NeRF rendering operation performed under a specific view . G is the rendering function used to generate 3D image data from the perspective of the view . So far, a set of textured point clouds V = {V(β ref ), V(β1),..., V(β N )} has been established. Although V(β ref ) already has high-fidelity textures projected from the reference image, other points occluded in the reference view still suffer from smoothed textures from the rough NeRF. To enhance the textures, the textures of other points are optimized, and the new view rendering is constrained using a diffusion prior. Specifically, a 19-dimensional descriptor F is optimized for each point, and its first three dimensions are initialized with the initial RGB colors. To avoid noisy colors and artifacts, a multi-scale deferred rendering scheme is adopted. In particular, given a new view β, the point cloud V is rasterized K times to obtain K feature maps, which are then concatenated and rendered into an image.

[0117] The specific process of the image output sub-module outputting the optimized rendered image by performing color correction and resolution adjustment on the image with detailed optimization is as follows:

[0118] The image output sub-module performs final color correction and resolution adjustment on the image with detailed optimization to adapt to different display devices and output requirements. The color correction is based on the standard configuration in the color management system, adjusting each color channel to achieve real-world color representation. The resolution adjustment is based on the requirements of the output medium, adjusting the image resolution through sampling and resampling techniques to ensure that the image maintains the best quality at various sizes. The output optimized rendered image thus has high-quality color accuracy and clarity.

[0119] In addition, the GPU cost can be reduced by combining the diffusion model and the InstructBLIP model, by optimizing the image processing flow in the generation task. The diffusion model is mainly used in high-fidelity 3D image generation to gradually construct and refine the details of the image, gradually transforming from a disordered noise state to a detailed image output through an iterative process. This model is particularly suitable for generating complex 3D textures and details because its iterative process allows precise control of the generated details at each step, thus generating extremely realistic images. Since its generation process simulates the physical diffusion process, it can well handle visual effects such as lighting, reflection, and texture while maintaining the naturalness of the image. The InstructBLIP model can generate or improve 3D images according to language instructions by combining techniques of natural language processing and visual data processing. This model is suitable for applications that require converting detailed language descriptions into precise 3D models, such as virtual reality, game development, and industrial design. It can quickly generate or modify 3D images according to specific user instructions. Compared with traditional large language-image pre-training models, the InstructBLIP model is more efficient in resource use. By optimizing image and text processing algorithms, this model reduces the dependence on high-performance computing resources, especially in GPU-intensive operations, and can significantly reduce costs and energy consumption.

[0120] Please refer to Figure 2 and Figure 8 , the artificial intelligence security and image anti-counterfeiting module includes:

[0121] Based on the optimized-rendered image, the security analysis sub-module conducts detection of the image content, screens for sensitive content in the image. The sensitive content includes privacy data and violation information, marks the content that needs to be reviewed. The specific process of generating the security analysis result is as follows:

[0122] The optimized image becomes the starting point for system processing. The system analyzes each pixel one by one through multi-level image scanning technology. In this way, the system can comprehensively cover and scan the entire image data and detect sensitive content in each area. During this process, the data in each area is precisely analyzed and recorded, and the system uses advanced algorithms to identify and classify suspicious areas. This fine scanning process ensures the comprehensive identification of sensitive content. Subsequently, according to the preset content classification criteria, the system further subdivides and prepares for the review of these sensitive contents, and finally forms a comprehensive security analysis result.

[0123] Based on the security analysis result, the content review sub-module checks the sensitive content marked in the image, conducts a review according to pre-trained data or a preset artificial intelligence model, deletes the image content that does not meet the standards. The specific process of obtaining the review confirmation result is as follows:

[0124] After obtaining the preliminary safety analysis results, the review system begins to perform a comprehensive inspection on the image content. During this process, the system compares each identified sensitive content in the image with the internal release standards of the organization. Through detailed comparison and verification work, it ensures that every element in the image meets the specified requirements. This series of precise comparison and review operations enables the identification and removal of non-compliant content one by one, thereby ensuring the purity of the image content. Through this meticulous inspection process, the accuracy and reliability of the image review and confirmation results are ultimately ensured.

[0125] Based on the review and confirmation results, the anti-counterfeiting coding sub-module inserts a digital watermark into the image, sets up a monitoring program to track the distribution and use of the image, and ensures that the source and distribution records of the image are queryable. The specific process for obtaining the anti-counterfeiting image data is as follows:

[0126] According to the review and confirmation results, the anti-counterfeiting coding system immediately starts to embed a digital watermark in the image. This operation is completed through a specially designed coding technology, ensuring that the embedding position and pattern of the digital watermark are precisely calculated, which not only ensures that the image quality is not affected but also ensures the security of the image. At the same time, the system sets up an image monitoring program to monitor the distribution and use of the image in real-time. This program can record the details of each access and use of the image, ensuring that the source and purpose of the image can be traced and verified. Through this series of operations, a complete anti-counterfeiting image data system is formed.

[0127] Embodiment 2

[0128] Based on the same inventive concept, this embodiment discloses a three-dimensional image security processing method based on a diffusion model, which is implemented based on the three-dimensional image security processing system described in Embodiment 1. The method includes the following steps:

[0129] S1: Perform coordinate alignment, resolution calibration, and format conversion on the image data obtained by three-dimensional scanning to generate normalized three-dimensional data;

[0130] S2: Based on the diffusion model and the normalized three-dimensional data, construct a deep learning model, and train the image data to learn the feature and structural information of the image to obtain the trained deep model;

[0131] S3: Use the trained deep model to analyze the shape, size, and relative position of the three-dimensional objects in the image, reconstruct the three-dimensional space, and obtain three-dimensional space image data;

[0132] S4: Based on the three-dimensional space image data, simulate the influence of the light source on the image, calculate the light scattering, reflection, and shadow effects after the interaction of light with the three-dimensional objects, and obtain the light simulation data;

[0133] S5: Based on the light simulation data, adjust the rendering parameters, perform image rendering processing, and output the optimized rendered image;

[0134] S6: Use artificial intelligence to perform security analysis and content review on the optimized rendered image, and generate anti-counterfeiting image data by embedding digital watermarks and performing image content monitoring.

[0135] Since the method introduced in the second embodiment of the present invention is the method implemented by the three-dimensional image security processing system based on the diffusion model in the first embodiment of the present invention, based on the system introduced in the first embodiment of the present invention, those skilled in the art can understand the specific implementation steps of this method, so it will not be elaborated here. Any method implemented based on the system in the first embodiment of the present invention falls within the scope of protection of the present invention.

[0136] Embodiment Three

[0137] Based on the same inventive concept, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method described in Embodiment One is implemented.

[0138] Since the computer-readable storage medium introduced in the third embodiment of the present invention is the computer-readable storage medium used to implement the three-dimensional image security processing method based on the diffusion model in the second embodiment of the present invention, based on the method introduced in the first embodiment of the present invention, those skilled in the art can understand the specific structure and variations of this computer-readable storage medium, so it will not be elaborated here. Any computer-readable storage medium used in the method of the first embodiment of the present invention falls within the scope of protection of the present invention.

[0139] Embodiment Four

[0140] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in Embodiment One is implemented.

[0141] Since the computer device introduced in the fourth embodiment of the present invention is the computer device used to implement the three-dimensional image security processing method based on the diffusion model in the second embodiment of the present invention, based on the method introduced in the first embodiment of the present invention, those skilled in the art can understand the specific structure and variations of this computer device, so it will not be elaborated here. Any computer device used in the method of the first embodiment of the present invention falls within the scope of protection of the present invention.

[0142] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0143] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0144] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A three-dimensional image security processing system based on a diffusion model, characterized in that The system includes: An image format conversion module, which is used to perform coordinate alignment, resolution calibration, and format conversion on the image data obtained by three-dimensional scanning to generate normalized three-dimensional data; An image feature training module, which is used to construct a deep learning model based on a diffusion model and the normalized three-dimensional data, and train the image data to learn the feature and structural information of the image, obtaining a trained deep model. Among them, the trained deep model is used to identify the key structural elements in the image and predict the positions and mutual relationships of these elements in the three-dimensional space; An image three-dimensional structure analysis module, which is used to utilize the trained deep model to analyze the shape, size, and relative position of the three-dimensional object in the image, reconstruct the three-dimensional space, and obtain three-dimensional space image data; An image light effect simulation module, which is used to simulate the influence of the light source on the image based on the three-dimensional space image data, calculate the light scattering, reflection, and shadow effects after the interaction between the light and the three-dimensional object, and obtain light simulation data; An image rendering module, which is used to adjust the rendering parameters based on the light simulation data, perform image rendering processing, and output an optimally rendered image; An artificial intelligence security and image anti-counterfeiting module, which is used to perform security analysis and content review on the optimally rendered image using artificial intelligence, and generate anti-counterfeiting image data by embedding digital watermarks and performing image content monitoring; Among them, the image feature training module includes: A model construction sub-module, which is used to select a network layer structure and an activation function based on the normalized three-dimensional data, define input and output nodes and the number of network layers, and generate an initial deep learning model framework; A feature learning sub-module, which is used to adopt the initial deep learning model framework, input three-dimensional image data for feature extraction, analyze the structural information and texture features of the image, optimize the model parameters, and generate a feature-optimized model; A model training sub-module, which is used to perform multiple rounds of training based on the feature-optimized model, adjust the learning rate and the loss function, and use the batch training method to optimize the generalization ability of the model, generating a trained deep model. Among them, in each round of training, the diffusion model is used to calculate the loss based on the difference between the current noise prediction and the actual noise, so as to guide the model to learn how to process the noise term at different time steps, where the noise term is used to reflect the noise accumulation effect based on the time step; before formal training, a text-to-image diffusion model is used to guide the three-dimensional representation, noise is introduced during the image rendering process, the noise is controlled by adjusting the attenuation coefficient and the noise standard deviation, and the score distillation loss L is used to push the rendered image closer to the higher density region based on the text embedding; The image three-dimensional structure analysis module includes: An image shape analysis sub-module, which is used to draw the contour of the three-dimensional object based on the trained deep model, perform shape analysis by comparing the drawn contour line with the image edge alignment, record the shape features, and generate image shape data; An image size analysis sub-module, which is used to measure the size of three-dimensional objects based on the image shape data. By measuring the height, width, and depth of each three-dimensional object, comparing with the standard size reference in the three-dimensional model, statistically summarizing the size data, and obtaining the image size data; An image position analysis sub-module, which is used to locate the position of three-dimensional objects in space through the image size data. According to the spatial relationship and depth information between three-dimensional objects, record the coordinate information of each three-dimensional object, and update the data in the three-dimensional model to obtain the three-dimensional space image data; The image rendering module includes: A parameter adjustment sub-module, which is used to analyze the light intensity and color distribution in the image based on the light simulation data, adjust the parameters one by one to match the predetermined visual effect, and adjust the color saturation and brightness level to generate image parameter optimization data. In the process of generating the image parameter optimization data, a diffusion CLIP loss is used to force the generated model to match the reference image; A detail enhancement sub-module, which is used to use the image parameter optimization data to perform hierarchical separation, contrast and sharpness adjustment on the details in the image, and optimize the key visual elements in the image, including texture and edges, to obtain a detail-optimized image; An image output sub-module, which is used to perform color correction and resolution adjustment on the detail-optimized image, and output the optimized rendered image; The artificial intelligence security and image anti-counterfeiting module includes: A security analysis sub-module, which is based on the optimized rendered image to detect the image content, screen the sensitive content in the image. The sensitive content includes privacy data and violation information, identify the content that needs to be reviewed. The specific process of generating the security analysis result is: A content review sub-module, which checks the sensitive content marked in the image according to the security analysis result, and conducts a review according to the pre-trained data or a preset artificial intelligence model, deletes the image content that does not meet the standards, and obtains the review confirmation result; An anti-counterfeiting coding sub-module, which inserts a digital watermark into the image through the review confirmation result, and sets up a monitoring program to track the distribution and use of the image to obtain the anti-counterfeiting image data.

2. The 3D image security processing system based on the diffusion model according to claim 1, wherein, The image format conversion module includes: A coordinate alignment sub-module, which is used to check and align the spatial coordinates based on the image data obtained by three-dimensional scanning, and generate the aligned image data; A resolution calibration sub-module, which is used to detect and adjust the image pixel resolution of the aligned image data, and generate the calibrated image data; A data formatting sub-module, which performs data format conversion operations based on the calibrated image data, converts the image data into a unified point cloud format, and generates the normalized three-dimensional data.

3. The 3D image security processing system based on the diffusion model according to claim 1, characterized in that The image light effect simulation module includes: A light source setting sub-module, which is used to configure the light source based on the three-dimensional space image data through the BRDF model to simulate the effect of natural light or indoor lighting, adjust the light source position and intensity, and set multiple lighting angles and color temperatures to generate the light source configuration data. The BRDF model is a bidirectional reflectance distribution function model; A light interaction sub-module, which is used to analyze the interaction between light and the surface of a three-dimensional object by using the light source configuration data, calculate the reflection and scattering effects, adjust the light interaction behavior according to the material and surface characteristics of the object, simulate the performance of light in various environments, and generate light environment interaction data; A shadow generation sub-module, which is used to locate the shadow area generated by the obstruction between the light source and the object through the light environment interaction data, calculate the intensity and extension of the shadow, adjust the blur degree and boundary of the shadow, optimize the light-dark contrast in the image, and obtain light simulation data.

4. The three-dimensional image security processing system based on a diffusion model according to claim 3, characterized in that The BRDF model is calculated according to the formula: Calculate the relationship between the incident light and the reflected light when incident from multiple angles, and simulate the effects of natural light or indoor lighting; Among them, the function represents the bidirectional reflectance distribution function, which is used to calculate the light reflectance of a given material surface in the incident light direction and the reflected light direction . is the incident light direction, is the reflected light direction, is the incident angle, is the viewing angle, is the material property, is the diffuse reflection coefficient, which represents the proportion of the diffuse reflection part of the material surface, is the cosine value of the angle, which is used to calculate the effective component of the light incident on the surface, is the specular reflection coefficient, which is used to represent the proportion of the specular reflection part of the material surface, is the specular highlight attenuation factor, which adjusts the intensity of the specular reflection according to the viewing angle , so that the highlight effect weakens as the viewing angle increases, is the light radiation intensity in the unit reflection direction, which characterizes the light brightness per unit area in the reflection direction , is the light intensity in the unit incident direction, which characterizes the light energy received per unit area in the incident direction .

5. The 3D image security processing system based on the diffusion model according to claim 1, characterized in that, The image rendering module includes: A parameter adjustment sub-module, which is used to analyze the light intensity and color distribution in the image based on the light simulation data, adjust the parameters one by one to match the predetermined visual effect, and adjust the color saturation and brightness level to generate image parameter optimization data; A detail enhancement sub-module, which is used to hierarchically separate the details in the image, adjust the contrast and sharpness by using the image parameter optimization data, and optimize the key visual elements in the image, including textures and edges, to obtain an image with optimized details; An image output sub-module, which is used to perform color correction and adjust the resolution on the image with optimized details, and output the optimized rendered image.

6. A three-dimensional image security processing method based on a diffusion model, characterized in that, Implemented based on the three-dimensional image security processing system based on the diffusion model according to any one of claims 1 to 5, the method includes the following steps: S1: Align the coordinates, calibrate the resolution, and convert the format of the image data obtained by three-dimensional scanning to generate normalized three-dimensional data; S2: Construct a deep learning model based on the diffusion model and the normalized three-dimensional data, and train the image data to learn the feature and structure information of the image to obtain a trained deep model, wherein the trained deep model is used to identify the key structural elements in the image and predict the positions and mutual relationships of these elements in the three-dimensional space; S3: Use the trained deep model to analyze the shape, size, and relative position of the three-dimensional object in the image, reconstruct the three-dimensional space, and obtain three-dimensional space image data; S4: Based on the three-dimensional space image data, simulate the influence of the light source on the image, calculate the light scattering, reflection, and shadow effects after the interaction between the light and the three-dimensional object, and obtain light simulation data; S5: Based on the light simulation data, adjust the rendering parameters, perform image rendering processing, and output the optimized rendered image; S6: Perform security analysis and content review on the optimized rendered image by using artificial intelligence, and generate anti-counterfeiting image data by embedding digital watermarks and performing image content monitoring; Among them, constructing a deep learning model based on the diffusion model and the normalized three-dimensional data, and training the image data to learn the feature and structure information of the image to obtain a trained deep model includes: Based on the normalized three-dimensional data, select the network layer structure and activation function, define the input and output nodes and the number of network layers, and generate an initial deep learning model framework; Using the described initialized deep learning model framework, input three-dimensional image data for feature extraction, analyze the structural information and texture features of the image, optimize the model parameters, and generate a feature-optimized model; Based on the feature-optimized model, conduct multiple rounds of training, adjust the learning rate and loss function, and use the batch training method to optimize the generalization ability of the model, generating a trained deep model. Among them, in each round of training, use the diffusion model to calculate the loss based on the difference between the current noise prediction and the actual noise, so as to guide the model to learn how to process noise terms at different time steps, where the noise terms are used to reflect the noise accumulation effect based on time steps; before formal training, use a text-to-image diffusion model to guide three-dimensional representation, introduce noise during the process of rendering images, control the noise by adjusting the attenuation coefficient and noise standard deviation, and use the score distillation loss L to push the rendered image closer to the higher-density area based on text embedding; Using the trained deep model, analyze the shape, size, and relative position of three-dimensional objects in the image, reconstruct the three-dimensional space, and obtain three-dimensional space image data, including: Based on the trained deep model, draw the contours of three-dimensional objects, conduct shape analysis by comparing the drawn contour lines with the image edges, record the shape features, and generate image shape data; Based on the image shape data, measure the dimensions of three-dimensional objects. By measuring the height, width, and depth of each three-dimensional object and comparing with the standard dimension reference in the three-dimensional model, statistically summarize the dimension data to obtain image dimension data; Through the image dimension data, locate the positions of three-dimensional objects in space. According to the spatial relationship and depth information between three-dimensional objects, record the coordinate information of each three-dimensional object and update the data in the three-dimensional model to obtain three-dimensional space image data; Based on the ray simulation data, adjust the rendering parameters and perform image rendering processing to output an optimized-rendered image, including: Based on the ray simulation data, analyze the light intensity and color distribution in the image, adjust the parameters one by one to match the predetermined visual effect, and adjust the color saturation and brightness levels to generate image parameter optimization data. Among them, during the process of generating image parameter optimization data, use the diffusion CLIP loss to force the generated model to match the reference image; Use the image parameter optimization data to perform hierarchical separation of details in the image, adjust the contrast and sharpness, and optimize the key visual elements in the image, including texture and edges, to obtain an image with optimized details; Through the image with optimized details, perform color correction and adjust the resolution to output an optimized-rendered image; Perform security analysis and content review on the optimized-rendered image using artificial intelligence. By embedding digital watermarks and performing image content monitoring, generate anti-counterfeiting image data, including: Through the security analysis sub-module, based on the optimized-rendered image, conduct image content detection, screen for sensitive content in the image. Sensitive content includes privacy data and violation information, identify the content that needs to be reviewed. The specific process of generating the security analysis result is as follows: The content review sub-module checks the sensitive content identified in the image according to the security analysis results, conducts a review based on pre-trained data or a preset artificial intelligence model, deletes the image content that does not meet the standards, and obtains the review confirmation result; The anti-counterfeiting coding sub-module inserts a digital watermark into the image according to the review confirmation result, sets up a monitoring program to track the distribution and use of the image, and obtains the anti-counterfeiting image data.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the three-dimensional image security processing method based on the diffusion model as described in claim 6.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the three-dimensional image security processing method based on the diffusion model as described in claim 6.

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