Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
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Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
- 1. Journal First Paper ACM Transactions on Software Engineering and Methodology Date of acceptance: July 2022 DOI: https://doi.org/10.1145/3550271 Replication package: https://zenodo.org/record/6619279 Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering Mohammed Attaoui1, Hazem Fahmy1, Fabrizio Pastore1, Lionel Briand1,2 1University of Luxembourg, 2University of Ottawa
- 2. Image by NVIDIA ©
- 3. Safety-critical systems need to be trusted
- 4. Trustworthiness = Certification + Explanation 1 Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi. 2020. A survey of safety and trustworthiness of deep neural networks: Veriication, testing, adversarial attack and defence, and interpretability. Computer Science Review 37 (2020), 100270.
- 5. Trustworthiness = Certification + Explanation
- 6. Trustworthiness = Certification + Explanation ISO 26262 and ISO/PAS 21448 enforce the identification of the situations in which the system might be unsafe and the design of countermeasures to put in place
- 7. Lot of failure-inducing inputs
- 8. Similar images capture a same failure scenario Cluster 1 Cluster 2
- 9. To classify like a human use a DNN Feature Extraction
- 10. Safety Analysis Based on Feature Extraction Failure Inducing Images Data Preprocessing Features Extraction (VGG16) Clustering (DBSCAN) Dimensionality Reduction (PCA) Unsafe-set Selection Retraining Improve d DNN Safety Analysis Root cause clusters Detection of Unsafe Scenarios Image Analysis Cluster 1 Cluster 2 Cluster 3 Unlabeled Improvement set Labeling Model Improvement Mid-Left Bottom-Left Middle-Left Bottom-Left Top-Right
- 11. Safety Analysis Based on Feature Extraction Failure Inducing Images Data Preprocessing Features Extraction (VGG16) Dimensionality Reduction (PCA) Image Analysis
- 12. Safety Analysis Based on Feature Extraction Failure Inducing Images Data Preprocessing Features Extraction (VGG16) Clustering (DBSCAN) Dimensionality Reduction (PCA) Unsafe-set Selection Retraining Improve d DNN Safety Analysis Root cause clusters Detection of Unsafe Scenarios Image Analysis Cluster 1 Cluster 2 Cluster 3 Unlabeled Improvement set Labeling Model Improvement Mid-Left Bottom-Left Middle-Left Bottom-Left Top-Right
- 13. Clustering (DBSCAN) Safety Analysis Root cause clusters Detection of Unsafe Scenarios Cluster 1 Cluster 2 Cluster 3 Safety Analysis Based on Feature Extraction
- 14. Safety Analysis Based on Feature Extraction Failure Inducing Images Data Preprocessing Features Extraction (VGG16) Clustering (DBSCAN) Dimensionality Reduction (PCA) Unsafe-set Selection Retraining Improve d DNN Safety Analysis Root cause clusters Detection of Unsafe Scenarios Image Analysis Cluster 1 Cluster 2 Cluster 3 Unlabeled Improvement set Labeling Model Improvement Mid-Left Bottom-Left Middle-Left Bottom-Left Top-Right
- 15. Safety Analysis Based on Feature Extraction Unsafe-set Selection Retraining Improved DNN Unlabeled Improvement set Labeling Model Improvement Mid-Left Bottom-Left Middle-Left Bottom-Left Top-Right
- 16. Safety Analysis Based on Feature Extraction Failure Inducing Images Data Preprocessing Features Extraction (VGG16) Clustering (DBSCAN) Dimensionality Reduction (PCA) Unsafe-set Selection Retraining Improve d DNN Safety Analysis Root cause clusters Detection of Unsafe Scenarios Image Analysis Cluster 1 Cluster 2 Cluster 3 Unlabeled Improvement set Labeling Model Improvement Mid-Left Bottom-Left Middle-Left Bottom-Left Top-Right
- 17. Empirical Evaluation Gaze Detection DNN Closed Eyes Detection DNN Head pose Detection DNN Traffic sign Detection DNN Object Detection DNN Face landmarks Detection DNN
- 18. Experiments with simulator-based images have shown that the generated clusters include images with a common characteristic that is a plausible cause for failures. User study have shown that the inspection of five images per cluster is sufficient to determine failure cause correctly. Much quicker than inspecting all failure-inducing images. Improve also models with small room for improvement. Always better than any baseline approach.
- 19. Journal First Paper ACM Transactions on Software Engineering and Methodology Date of acceptance: July 2022 DOI: https://doi.org/10.1145/3550271 Replication package: https://zenodo.org/record/6619279 Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering Mohammed Attaoui1, Hazem Fahmy1, Fabrizio Pastore1, Lionel Briand1,2 1University of Luxembourg, 2University of Ottawa