A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation
Abstract
:1. Introduction
- (1)
- The proposed framework consists of an access control layer, a storage layer, a model training layer, and a model evaluation layer. The access control layer implements secure resource sharing using access control policies. To achieve fine-grained and flexible access control, the attribute-based access control model is adopted as the access control model, and user permissions are packaged as user role attributes using the principles of role-based access control. To decentralize and dynamically manage the access control model, a smart contract, Policy Contract, is designed to implement the above model, and the access control policies are stored in the blockchain ledger. The storage layer enables efficient and secure storage of resources. Resource files are stored using off-chain auxiliary storage based on the InterPlanetary File System (IPFS), and the encrypted results of index addresses of the resource files in the IPFS are stored in the blockchain ledger. To decentralize and efficiently manage the on-chain records of resources, a smart contract, Data and model record Contract (DC), is designed. The model training layer performs model training on the users’ server, and to ensure security, the training data must come from the storage layer. The model evaluation layer uses the data from the storage layer to evaluate the models stored in the storage layer. To achieve decentralized evaluation and ensure scores are immutable, a method is designed in DC to perform model evaluation, and the scores are automatically uploaded to the blockchain as a resource attribute.
- (2)
- In the experimental section, the proposed framework is applied to the research of deep learning-based Motion Object Segmentation (MOS). Based on the aforementioned design, a blockchain-based deep model evaluation framework is constructed, which can be used for training, sharing, and evaluating deep MOS models. The experiment is set up with two users, the CDnet2014 dataset, and the FgSegNet_v2 model, demonstrating the key functionalities of the proposed framework. In the analysis part, the effectiveness of the framework’s storage strategy is validated, and the trustworthiness of the framework is also discussed.
2. Related Works
2.1. Deep Learning Model Research and Its Trustworthiness Issues
2.2. Deep Model Research for Motion Object Segmentation and Its Trustworthiness Issues
2.3. Blockchain Technology
2.4. Blockchain for Deep Learning
3. Proposed Blockchain-Based Trustable Model Evaluation Framework for Deep Learning
3.1. Access Control Layer
Algorithm 1 AddPolicy(): Adding an access control policy to the blockchain |
Require: Ensure: 1: if then 2: return 3: end if 4: 5: 6: if then 7: return 8: end if 9: 10: 11: |
12: return |
3.2. Storage Layer
Algorithm 2 AddResource(): Uploading a resource to the storage layer |
Require: Ensure: 1: 2: if then 3: return 4: end if 5: 6: 7: 8: if then 9: return 10: end if 11: |
12: return |
3.3. Model Training Layer
3.4. Model Evaluation Layer
Algorithm 3 ModelEvaluation(): Evaluating model performance and uploading scores to the blockchain |
Require: Ensure: 1: 2: if then 3: return 4: end if 5: /**Obtaining the models and data required for evaluation**/ 6: 7: 8: 9: 10: 11: 12: 13: 14: /**Evaluating model performance and uploading scores**/ 15: 16: 17: 18: 19: 20: 21: |
22: return |
4. Experiment and Analysis
4.1. Experimental Settings
4.2. Experimental Procedure
4.3. Analysis
4.3.1. Storage Strategy Analysis
4.3.2. Trustworthiness Analysis
5. Conclusions
- (1)
- To achieve secure resource sharing, access control technology is introduced in the framework. The access control model adopts the ABAC model and incorporates the ideas of the RBAC model to enable fine-grained and flexible access permission division. A smart contract called PC is designed to manage access control policies in a decentralized and efficient manner.
- (2)
- To achieve efficient and secure storage of resources, off-chain and on-chain storage approaches are combined. The IPFS is utilized for off-chain storage of resource files, while the encrypted results of their index addresses are stored in the blockchain. A smart contract called DC is designed to manage on-chain resource records in a decentralized and efficient manner.
- (3)
- To ensure the security of training, in the model training layer, it is required that the training data must have records in the blockchain.
- (4)
- To ensure the security of the evaluation process, the model evaluation layer must use the recorded data in the blockchain to evaluate the recorded models. A method in the smart contract DC is designed to enable decentralized evaluation and ensure the immutability of evaluation scores, which are automatically uploaded to the blockchain as a resource attribute.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Attribute | Types |
---|---|
Scene | land (outdoor/indoor), water, underwater, etc. |
Challenge | camera jitter, shadow, challenging weather, dynamic back- ground, intermittent object motion, low frame-rate, Pan-Tilt-Zoom camera (PTZ), night view, thermal imaging, air turbulence, occlusion, low-quality video, foreground size, etc. |
Sensor | color sensor, thermal sensor, depth sensor, etc. |
Camera | static camera, moving camera |
Format | grayscale, RGB |
Annotation | pixel-level mask, bounding box, etc. |
Attribute | Types (or Descriptions) |
---|---|
Training Set | collection of names of training videos |
Pre-training Method | transfer learning, self-supervised learning, etc. |
Training Method | supervised learning, supervised adversarial learning, weakly supervised learning, etc. |
Training Details | optimization algorithm, loss, batch size, maximum number of epochs, etc. |
Metric (Abbr.) | Formula |
---|---|
Precision (Pr) | |
Recall (Re) | |
F-Measure | |
Specificity (Sp) | |
False Positive Rate (FPR) | |
False Negative Rate (FNR) | |
Percentage of Wrong Classification (PWC) |
Function | Description |
---|---|
Uploading v to the blockchain and setting its index as x | |
Searching with index x from the blockchain state database | |
Retrieving the current time in Linux system | |
Encrypting x with key k | |
Decrypting x with key k | |
Uploading v to the IPFS and return its | |
Downloading the file with x from the IPFS | |
Packaging | |
Using a dataset d and an evaluation algorithm e to evaluate the performance of a model m | |
Adding a record r |
Requirements | Traditional Deep Model Evaluation Approach | Proposed Framework |
---|---|---|
Security of Resource Sharing | Centralized storage of resources; resources are either completely private or completely public with no access control management. | Distributed storage of resources based on the IPFS; resource addresses are encrypted and recorded on the blockchain, and the management of resource records is governed by access control. |
Security of Model Training | No explicit mechanism to ensure the security of training data. | The training data must be recorded on the blockchain. |
Correctness of Model Evaluation | No explicit mechanism to ensure the security of the data and models used for evaluation. | The data and models used for evaluation must be recorded on the blockchain. |
Decentralization of Model Evaluation | If the model is completely private, the model evaluation can only be performed by the owner of the model. If the model is completely public, the model evaluation can be done by any user. | The model evaluation requests are initiated by users who have access permissions to the corresponding resources, and are executed by a smart contract on the blockchain. |
Immutability of Model Evaluation Results | The model evaluation results are recorded by the evaluator, and there is a risk of tampering. | The model evaluation results are automatically uploaded to the blockchain as a resource attribute. |
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Share and Cite
Jiang, R.; Li, J.; Bu, W.; Shen, X. A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation. Sensors 2023, 23, 6492. https://doi.org/10.3390/s23146492
Jiang R, Li J, Bu W, Shen X. A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation. Sensors. 2023; 23(14):6492. https://doi.org/10.3390/s23146492
Chicago/Turabian StyleJiang, Rui, Jiatao Li, Weifeng Bu, and Xiang Shen. 2023. "A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation" Sensors 23, no. 14: 6492. https://doi.org/10.3390/s23146492
APA StyleJiang, R., Li, J., Bu, W., & Shen, X. (2023). A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation. Sensors, 23(14), 6492. https://doi.org/10.3390/s23146492