A method and system for embedding a visible watermark in a ciphertext domain image based on swarm intelligence optimization
By embedding visible watermarks into encrypted images and utilizing swarm intelligence optimization, the problem of generating copyright identifiers on encrypted data has been solved, achieving a balance between security, visual prominence, and functional expansion, thereby improving the efficiency of copyright management and user experience in smart libraries.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUNAN FIRST NORMAL UNIV
- Filing Date
- 2026-02-06
- Publication Date
- 2026-05-29
Smart Images
Figure CN121664934B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of encrypted storage technology, specifically to a method and system for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization. Background Technology
[0002] In scenarios such as smart libraries and cloud photo albums, user image resources are often stored in encrypted form in the cloud to prevent privacy leaks. Against this backdrop, how to effectively protect and manage the copyright of these encrypted images has become an important issue. Existing technologies mainly employ two approaches, but both have significant shortcomings:
[0003] 1. Invisible Watermark / Information Hiding in Ciphertext Domain. This technology embeds invisible authentication information or digital watermarks into encrypted images. The technical problem is that the copyright identifier is "hidden," requiring specific detection or extraction algorithms to verify copyright ownership or track infringement. This fails to meet the demand for immediate and intuitive copyright notices in smart libraries where resources are openly accessed and quickly shared, resulting in a poor user experience and low management efficiency.
[0004] 2. Add a visible watermark before encryption. This is an intuitive but simple approach. However, its technical problems are as follows: First, the watermark is added during the plaintext stage, and the addition process (such as strength and position) is completely independent of the subsequent encryption process. It cannot be optimized based on the characteristics of the encrypted text, potentially leading to security vulnerabilities or poor visual effects. Second, and more fundamentally, this method does not achieve "copyright identification in encrypted form." The watermark is irreversible for all users, making it unable to flexibly support tiered services for different user groups.
[0005] Therefore, existing technologies lack an integrated solution that can directly manipulate encrypted data, generate visually visible copyright marks, and intelligently optimize the marks to simultaneously meet security, visual, and functional expansion requirements.
[0006] To address the aforementioned issues, there is an urgent need for a method and system for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization, which can solve the problems existing in traditional methods. Summary of the Invention
[0007] The purpose of this invention is to provide a method and system for embedding visible watermarks in encrypted images based on swarm intelligence optimization. This method directly embeds visible watermarks into encrypted images and automatically balances security, visual salience, and functional reservation through swarm intelligence optimization, thereby achieving a balance between security and intuitive copyright declaration.
[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0009] A method for embedding visible watermarks in ciphertext domain images based on swarm intelligence optimization includes:
[0010] Step 1: Obtain the original plaintext image, and encrypt the original plaintext image using a preset encryption algorithm to generate a ciphertext image;
[0011] Step 2: Perform residual entropy space modeling on the encrypted image and generate a visible watermark image based on the copyright information; construct a watermark embedding parameter set to be optimized based on the visible watermark image, and encode the watermark embedding parameter set into individual position vectors in the swarm intelligence algorithm;
[0012] Step 3: Perform a multi-objective optimization search based on swarm intelligence;
[0013] Step 4: Based on the search results, perform optimal parameter selection set ciphertext field watermark embedding to obtain a ciphertext image with a visible watermark;
[0014] Step 5: Securely distribute and process the encrypted image with a visible watermark.
[0015] Furthermore, the preset encryption algorithm is:
[0016] The original plaintext image I is encrypted using a stream cipher algorithm to generate a pseudo-random sequence K of the same length as the number of pixels in the image. This sequence is used as the key and encrypted using a bitwise XOR operation, i.e., E(i,j) = I(i,j) ⊕ K(i,j), where (i,j) are the pixel coordinates.
[0017] Furthermore, the preset encryption algorithm is:
[0018] The original plaintext image is encrypted using block ciphers or chaotic encryption algorithms.
[0019] Furthermore, in step 2, residual entropy space modeling is performed on the ciphertext image, specifically as follows:
[0020] Divide the encrypted image into multiple non-overlapping image blocks and calculate the residual entropy of each image block;
[0021] A global residual entropy space model is constructed based on the residual entropy of each image patch.
[0022] Furthermore, in step 2, a visible watermark image is generated based on the copyright information, specifically as follows:
[0023] Generate a binary or grayscale visible watermark image based on copyright information, which includes the library logo and text samples.
[0024] Furthermore, in step 2, the watermark embedding parameter set includes the global transparency factor of the watermark and the position of the watermark in the image.
[0025] Furthermore, in step 3, a multi-objective optimization search based on swarm intelligence is performed, specifically as follows:
[0026] Initialize a population containing multiple individuals;
[0027] Define a multi-objective fitness function, which includes at least a first sub-objective function for evaluating the security of the ciphertext after embedding the watermark and a second sub-objective function for evaluating the visual saliency of the watermark in the decrypted image;
[0028] A multi-objective swarm intelligence optimization algorithm is used to drive population evolution and search for Pareto optimal solution sets, where each solution represents a set of watermark embedding parameters that achieve a trade-off among multiple objectives.
[0029] Furthermore, in step 4, the optimal parameter selection set for ciphertext domain watermark embedding is performed based on the search results to obtain a ciphertext image with a visible watermark, specifically:
[0030] Select the final solution from the Pareto optimal solution set;
[0031] Using the parameters corresponding to the final solution, the visible watermark image is directly embedded into the ciphertext image to generate a ciphertext image with a visible watermark.
[0032] Furthermore, in step 5, the encrypted image with a visible watermark undergoes secure distribution and service processing, specifically as follows:
[0033] Store encrypted images with visible watermarks in a cloud database;
[0034] In response to user requests and user permissions, provide the user with a decrypted image or the original image with a visible watermark.
[0035] This invention also provides a ciphertext-domain image visible watermark embedding system based on swarm intelligence optimization, applied to the aforementioned ciphertext-domain image visible watermark embedding method based on swarm intelligence optimization, comprising:
[0036] The image encryption and preprocessing module is used to encrypt the acquired raw plaintext image to generate a ciphertext image;
[0037] A watermark optimization and embedding control center, connected to the image encryption and preprocessing module, includes:
[0038] The watermark generation unit is used to generate a visible watermark image;
[0039] The residual entropy modeling unit is used to perform residual entropy space modeling on the ciphertext image;
[0040] A swarm intelligence optimization engine for performing swarm intelligence-based multi-objective optimization searches;
[0041] An embedded execution unit is used to select the optimal parameters and embed the ciphertext field watermark based on the search results, so as to obtain a ciphertext image with a visible watermark.
[0042] The secure storage and distribution service module connects to the watermark optimization and embedding control center and is used to securely distribute and process encrypted images with visible watermarks.
[0043] In summary, the present invention has at least one of the following beneficial technical effects:
[0044] 1. Pioneering the embedding of a watermark visible in the encrypted field, achieving a balance between security and public declaration:
[0045] Copyright identification is completed directly within the encrypted data, and the decrypted images obtained by users come with watermarks, realizing instant copyright declaration of "what you see is what you own," which greatly improves the efficiency of image resource distribution in smart libraries and the intuitiveness of copyright prompts.
[0046] 2. Achieve automatic optimal trade-offs for multiple performance aspects through intelligent optimization:
[0047] It overcomes the shortcomings of traditional methods that rely on manual experience for parameter tuning and struggle to simultaneously meet multiple conflict performance indicators. It can automatically find the optimal or satisfactory watermark embedding scheme under given security models, visual quality requirements, and functional reservation requirements.
[0048] 3. Provide a scalable technological foundation for advanced smart library services:
[0049] The solution was designed with "reversibility" and "feature preservation" in mind, which makes the generated watermarked encrypted images naturally support or easily extend to advanced service modes such as "hierarchical browsing" and "encrypted image retrieval".
[0050] 4. Optimized preprocessing for efficient online service:
[0051] Although the optimization process is computationally intensive, it is a one-time preprocessing or offline processing step. Once the optimal parameters X* are found for a certain type of image or watermark, the actual embedding operation is very fast when batch processing a large number of images or serving online requests. This ensures the overall high efficiency of the system service. Attached Figure Description
[0052] Figure 1 This is a schematic diagram of the method flow of the present invention;
[0053] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation
[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.
[0055] like Figure 1 As shown, this invention provides a method for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization, comprising:
[0056] Step 1: Obtain the original plaintext image, and encrypt the original plaintext image using a preset encryption algorithm to generate a ciphertext image;
[0057] Step 2: Perform residual entropy space modeling on the encrypted image and generate a visible watermark image based on the copyright information; construct a watermark embedding parameter set to be optimized based on the visible watermark image, and encode the watermark embedding parameter set into individual position vectors in the swarm intelligence algorithm;
[0058] Step 3: Perform a multi-objective optimization search based on swarm intelligence;
[0059] Step 4: Based on the search results, perform optimal parameter selection set ciphertext field watermark embedding to obtain a ciphertext image with a visible watermark;
[0060] Step 5: Securely distribute and process the encrypted image with a visible watermark.
[0061] In step 1, the original plaintext image is obtained, and a preset encryption algorithm is used to encrypt the original plaintext image to generate a ciphertext image, specifically as follows:
[0062] The server receives the original plaintext image I, which has a size of M×N pixels;
[0063] The original plaintext image I is encrypted using a preset encryption algorithm to generate ciphertext E. This invention provides two preset encryption algorithms:
[0064] 1. Encrypt the original plaintext image I using a stream cipher algorithm to generate a pseudo-random sequence K of the same length as the number of pixels in the image. Use this sequence K as the key and encrypt it using a bitwise XOR operation, i.e.: E(i,j) = I(i,j) ⊕ K(i,j), where (i,j) are the pixel coordinates.
[0065] 2. Use block ciphers or chaotic encryption algorithms to encrypt the original plaintext image. For example, use Logistic chaotic mapping to generate chaotic sequences to scramble and spread image pixels.
[0066] In step 2, residual entropy space modeling is performed on the ciphertext image, specifically as follows:
[0067] Divide the ciphertext image E into non-overlapping image blocks of size b×b. For each ciphertext block Calculate its security capacity, i.e., residual entropy, which can be used to embed watermarks without significantly affecting security and recoverability. ( This invention provides a modeling method as follows:
[0068] ,in, It is the theoretical maximum entropy, such as for an 8-bit grayscale block. Bit, It is the actual entropy of the ciphertext block. The larger the remaining entropy, the more modifications the block can tolerate.
[0069] Construct a global residual entropy space model This model will serve as the theoretical basis for safety constraints in subsequent optimizations.
[0070] Encryption algorithms are not limited to stream ciphers or chaotic encryption. Any symmetric encryption algorithm that can produce ciphertext with a similar random distribution (such as AES in different operating modes) can be used as an alternative. The key is that the encrypted image data should have a high degree of randomness (entropy).
[0071] The quantitative model of "residual entropy" can be defined in different ways. For example, it can be built not based on information entropy, but on the variance of pixel values in the ciphertext block, the predictability of local statistical characteristics, or other measures of "modifiable space". The purpose is to provide a quantitative basis for security constraints in optimization.
[0072] In step 2, a visible watermark image is generated based on the copyright information, specifically as follows:
[0073] A binary or grayscale visible watermark image W is generated based on the copyright information. Its size is usually smaller than I. The copyright information includes library logos and text samples, etc.
[0074] In step 2, a watermark embedding parameter set to be optimized is constructed based on the visible watermark image. This watermark embedding parameter set is then encoded into individual position vectors in a swarm intelligence algorithm, specifically:
[0075] Define the watermark embedding parameter set to be optimized and encode it as the position vector X of an individual (such as a particle) in a swarm intelligence algorithm;
[0076] The parameter set includes at least: the global transparency factor α of the watermark and the position (x, y) of the watermark in the image.
[0077] A more refined parameter set may also include: a vector of local intensity adjustment coefficients corresponding to the ciphertext block. Used for different blocks Adaptive adjustment of watermark embedding strength;
[0078] In summary, an individual can be represented as: X = [α, x, y] or .
[0079] Step 3 involves performing a multi-objective optimization search based on swarm intelligence, specifically including the following steps:
[0080] 1. Initialize a population containing multiple individuals: Randomly generate an initial population containing P individuals. Each individual represents a set of possible watermark embedding parameters;
[0081] 2. Define the multi-objective fitness function: Design the function Used to assess individuals The advantages and disadvantages, It is a vector function containing multiple sub-objectives:
[0082] (1) Safety. The assessment is based on... The degree of change in the statistical properties of the encrypted image after embedding a watermark can be defined as a negative correlation function of the changes in the residual entropy of each block before and after the modification, for example: Where k=1 to T, It is the remaining entropy of the block after the watermark is embedded. The larger the value (the less negative), the smaller the impact on encryption security.
[0083] (2) Visibility. Evaluate the visual saliency of the watermark in the final decrypted image. This needs to be evaluated after simulated decryption (using the key). Indicators such as contrast and local differences in structural similarity (SSIM) can be combined. For example, it is desirable for the watermark area to have a moderate contrast with the background: ,in and These are the mean and standard deviation of pixel values in the watermark-covered area and the background area, respectively, after simulated decryption. The higher the value, the more prominent the watermark.
[0084] (3) Reversibility / Availability Guarantee. Evaluate whether the original image can still be recovered for authorized users or whether sufficient features can be preserved for content retrieval under the current embedding parameters. For example, the similarity between the low-frequency features of the modified image and the original features can be evaluated.
[0085] 3. Employ multi-objective swarm intelligence optimization algorithms (such as NSGA-II, MOPSO) to drive population evolution, specifically:
[0086] (1) Iteration: In each generation, calculate the fitness vector F( ) of all individuals. );
[0087] (2) Ranking and selection: Individuals are ranked according to Pareto dominance and crowding is calculated. Excellent individuals are selected to enter the next generation.
[0088] (3) Update: New individuals are generated through crossover, mutation (genetic algorithm) or velocity and position updates (particle swarm algorithm);
[0089] (4) Termination: Repeat the iteration until the maximum algebraic value is reached or the solution set converges, and finally output a Pareto optimal solution set. Each solution in this set achieves some optimal trade-off among multiple objectives.
[0090] Furthermore, it should be noted that the core optimization engine can employ any effective multi-objective optimization algorithm, such as Multi-Objective Ant Colony Optimization (MOACO), Multi-Objective Differential Evolution (MODE), Multi-Objective Simulated Annealing (MOSA), etc., as long as it can be used to solve the multi-objective optimization problem of watermark embedding parameters.
[0091] In step 4, the optimal parameter selection set is used to embed the ciphertext domain watermark based on the search results, resulting in a ciphertext image with a visible watermark, specifically:
[0092] 1. Decision: From the Pareto optimal solution set, select a final solution X* based on the actual strategy of the smart library (e.g., prioritizing security or prioritizing watermark visibility);
[0093] 2. Embedding operation: Using the parameters corresponding to X*, the visible watermark W is directly embedded into the ciphertext image E, generating a ciphertext image with a visible watermark. Specifically:
[0094] (1) Embedding example: For an image encrypted with a stream cipher XOR, the embedding operation can be performed by additive mixing on the ciphertext. Let the influence of the watermark W after scaling and translation be a matrix M mapped onto the ciphertext image, with the value range and transparency as follows. Local intensity adjustment coefficient vector If relevant, the embedding process can be represented as: or mod256 operates within a finite field;
[0095] (2) This step is the direct output of the invention. It also has two key attributes: data confidentiality (the original image cannot be decrypted without authorization) and copyright visibility (the watermark is directly displayed after authorization and decryption, without the need for additional detection).
[0096] The specific operations for embedding a watermark in the ciphertext field can be addition, multiplication, or replacing some bits (such as replacing the least significant bit of the ciphertext byte). As long as the operation can modulate the watermark information onto the ciphertext data in a controllable manner according to the optimization parameter X*, and can present a visible watermark pattern after decryption, it is considered an equivalent alternative implementation method.
[0097] In step 5, the encrypted image with a visible watermark undergoes secure distribution and service processing, specifically as follows:
[0098] Encrypted image with visible watermark Stored in a cloud database;
[0099] In response to user requests and based on user permissions, the system will provide the user with a decrypted image or the original image with a visible watermark. This can be categorized into the following situations:
[0100] When a user requests an image:
[0101] For general users: Using decryption key pairs in the cloud Decryption is then performed. Since the watermark information is encoded in the ciphertext, decryption naturally yields a plaintext image with a visible watermark, enabling immediate copyright declaration.
[0102] For authorized users: additional control information or keys can be provided, combined with reversible information hiding technology, to completely remove visible watermarks from the decrypted image and recover the original plaintext image I.
[0103] Supports retrieval: Due to the potential for feature preservation during optimization, it is possible to retrieve [data / resources]. Features can be extracted from partially decrypted data to support image content retrieval in ciphertext or semi-ciphertext domains.
[0104] like Figure 2 As shown, this invention also provides a ciphertext-domain image visible watermark embedding system based on swarm intelligence optimization, applied to the aforementioned ciphertext-domain image visible watermark embedding method based on swarm intelligence optimization, comprising:
[0105] The image encryption and preprocessing module is used to encrypt the acquired raw plaintext image to generate a ciphertext image;
[0106] A watermark optimization and embedding control center, connected to the image encryption and preprocessing module, includes:
[0107] The watermark generation unit is used to generate a visible watermark image;
[0108] The residual entropy modeling unit is used to perform residual entropy space modeling on the ciphertext image;
[0109] A swarm intelligence optimization engine for performing swarm intelligence-based multi-objective optimization searches;
[0110] An embedded execution unit is used to select the optimal parameters and embed the ciphertext field watermark based on the search results, so as to obtain a ciphertext image with a visible watermark.
[0111] The secure storage and distribution service module, connected to the watermark optimization and embedding control center, is used for the secure distribution and service processing of encrypted images with visible watermarks. It includes:
[0112] A watermarked encrypted image library used to store the final image. ;
[0113] The decryption service unit responds to user requests and... Decrypt and return a watermarked view;
[0114] Advanced service units can also be set up to provide authorized users with functions such as original image restoration or content-based encrypted image retrieval.
[0115] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0116] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0119] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.
Claims
1. A method for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization, characterized in that, include: Step 1: Obtain the original plaintext image, and encrypt the original plaintext image using a preset encryption algorithm to generate a ciphertext image; Step 2: Perform residual entropy space modeling on the encrypted image and generate a visible watermark image based on the copyright information; A watermark embedding parameter set to be optimized is constructed based on the visible watermark image, and the watermark embedding parameter set is encoded into individual position vectors in a swarm intelligence algorithm; residual entropy space modeling is performed on the ciphertext image, specifically as follows: Divide the encrypted image into multiple non-overlapping image blocks and calculate the residual entropy of each image block; A global residual entropy space model is constructed based on the residual entropy of each image patch; Step 3: Perform a multi-objective optimization search based on swarm intelligence; specifically: Initialize a population containing multiple individuals; A multi-objective fitness function is defined, which includes at least a first sub-objective function for evaluating the security of the ciphertext after embedding the watermark and a second sub-objective function for evaluating the visual saliency of the watermark in the decrypted image. The first sub-objective function is defined as a negative correlation function of the change in the residual entropy of each image block before and after modification, which is used to quantify the impact of the embedding operation on the statistical properties of the ciphertext. A multi-objective swarm intelligence optimization algorithm is used to drive population evolution and search for Pareto optimal solution set, where each solution represents a set of watermark embedding parameters that achieve a trade-off among multiple objectives. Step 4: Based on the search results, perform optimal parameter selection and ciphertext field watermark embedding to obtain a ciphertext image with a visible watermark; specifically: Select the final solution from the Pareto optimal solution set; Using the parameters corresponding to the final solution, the visible watermark image is directly embedded into the ciphertext image to generate a ciphertext image with a visible watermark. Step 5: Securely distribute and process the encrypted image with a visible watermark.
2. The method for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization according to claim 1, characterized in that, The preset encryption algorithm is: The original plaintext image I is encrypted using a stream cipher algorithm to generate a pseudo-random sequence K of the same length as the number of pixels in the image. This sequence is used as the key and encrypted using a bitwise XOR operation, i.e., E(i,j) = I(i,j) ⊕ K(i,j), where (i,j) are the pixel coordinates.
3. The method for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization according to claim 1, characterized in that, The preset encryption algorithm is: The original plaintext image is encrypted using block ciphers or chaotic encryption algorithms.
4. The method for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization according to claim 3, characterized in that, In step 2, a visible watermark image is generated based on the copyright information, specifically as follows: Generate a binary or grayscale visible watermark image based on copyright information, which includes the library logo and text samples.
5. The method for embedding visible watermarks in encrypted domain images based on swarm intelligence optimization according to claim 4, characterized in that, In step 2, the watermark embedding parameter set includes the global transparency factor of the watermark and the position of the watermark in the image.
6. The ciphertext domain image visible watermark embedding method based on swarm intelligence optimization according to claim 5, characterized in that, In step 5, the encrypted image with a visible watermark undergoes secure distribution and service processing, specifically as follows: Store encrypted images with visible watermarks in a cloud database; In response to user requests and user permissions, provide the user with a decrypted image or the original image with a visible watermark.
7. A swarm intelligence-optimized ciphertext domain image visible watermark embedding system, applied to the swarm intelligence-optimized ciphertext domain image visible watermark embedding method according to any one of claims 1-6, characterized in that, include: The image encryption and preprocessing module is used to encrypt the acquired raw plaintext image to generate a ciphertext image; A watermark optimization and embedding control center, connected to the image encryption and preprocessing module, includes: The watermark generation unit is used to generate a visible watermark image; The residual entropy modeling unit is used to perform residual entropy space modeling on the ciphertext image; A swarm intelligence optimization engine for performing swarm intelligence-based multi-objective optimization searches; An embedded execution unit is used to select the optimal parameters and embed the ciphertext field watermark based on the search results, so as to obtain a ciphertext image with a visible watermark. The secure storage and distribution service module connects to the watermark optimization and embedding control center and is used to securely distribute and process encrypted images with visible watermarks.