Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring

Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet, Joris Guérin·May 14, 2024

Summary

This study investigates the effectiveness of neural network runtime monitors in defending against unforeseen threats in safety-critical applications. It addresses the overlooked issue of selecting appropriate thresholds for binary decisions by examining the resilience of monitors when faced with data distributions different from training data, simulating novel threats, covariate shifts, and adversarial attacks. The research compares four threshold optimization methods for computer vision datasets, focusing on the Out-of-Model-Scope setting, and evaluates their performance using in-distribution and out-of-distribution threat data. The study finds that optimizing thresholds without prior knowledge of runtime threats (ID+T) generally performs well, with the ID+T+O approach showing competitive results. However, including generic threats in optimization can lead to suboptimal thresholds due to the varying nature of the data. The choice of effectiveness measures, such as F1 and g-mean, influences the balance between error detection and system availability. The research suggests that future work should explore more specific threat categories and the role of threat knowledge in hyperparameter tuning for improved performance in real-world scenarios.

Key findings

13

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of threshold optimization for neural network monitoring to enhance the robustness of monitors in detecting unforeseen threats during inference . This problem is not entirely new but has received little attention in previous studies, which often assume that the runtime data distribution mirrors the training distribution, a strong assumption that may not hold in real-world scenarios where monitors need to safeguard systems against potentially unforeseen threats . The research investigates the effectiveness of monitors in handling unknown threats without prior knowledge during threshold tuning and explores the integration of generic threats into the threshold optimization scheme to improve monitor robustness . By conducting rigorous experiments on various image datasets, the paper seeks to provide insights into these critical aspects of neural network monitoring to ensure the safety and reliability of systems utilizing neural networks .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to the effectiveness of different approaches in building threshold optimization datasets for neural network runtime monitoring. Specifically, the study aims to investigate the effectiveness of these approaches and their implications for real-world applications, focusing on the identification of optimal thresholds for monitors to reject unsafe predictions during inference . The research explores various aspects, including the handling of unforeseen threats, the integration of generic threats into the threshold optimization scheme, and the impact of different families of threats on the proposed strategies .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" proposes several new ideas, methods, and models in the field of neural network monitoring :

  1. Threshold Optimization Methodology: The paper introduces a methodology for threshold optimization in neural network monitoring. It involves constructing optimization sets with data samples related to the target threat (T) and other generic threats (O) to assess the performance of monitoring thresholds when multiple threats are considered .

  2. Experimental Design: The study conducts extensive experiments using three image classification datasets (CIFAR10, CIFAR100, SVHN) and two neural network architectures (DenseNet and ResNet). Four distinct monitoring techniques are implemented, including Mahalanobis, Outside-the-Box, Max Softmax Probability, and Energy methods .

  3. Effectiveness Measures Comparison: The paper compares different effectiveness measures for threshold tuning, such as g-mean and F1 with and without oversampling. It extends the analysis to include F1 without oversampling and evaluates the performance across various evaluation metrics .

  4. Threshold Optimization Sets Comparison: The study visually compares the distributions of monitoring scores for different optimization sets (ID, ID+T, ID+O) and the evaluation set. It analyzes the effectiveness of these approaches and their implications for real-world applications, highlighting the importance of prior knowledge of threats for optimal monitor performance .

  5. Conclusion and Implications: The research affirms that leveraging knowledge of anticipated threats (ID+T approach) leads to optimal thresholds for monitors. However, it acknowledges the impracticality of assuming prior threat knowledge in safety-critical applications. The study also explores the impact of including generic threat data in the optimization process, revealing potential compromises in monitor performance. This suggests a future research direction of integrating data samples from more narrowly defined threat categories for tailored monitor design . The paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" introduces novel approaches and methodologies for neural network monitoring, offering significant advancements over previous methods :

  6. Threshold Optimization Strategies: The study presents four distinct threshold optimization strategies: ID (In-Distribution), ID+T (In-Distribution with Target threat), ID+O (In-Distribution with Other generic threats), and ID+T+O (In-Distribution with Target and Other generic threats). These strategies aim to evaluate the effectiveness of monitors when the target threat is unknown, reflecting a more realistic scenario .

  7. Effectiveness Measures Comparison: The research compares different effectiveness measures, such as F1 with over-sampling and g-mean, to optimize thresholds on the optimization set. The choice of effectiveness measure depends on the monitor's specific objectives, with F1 emphasizing missed errors reduction and g-mean prioritizing system availability by reducing false rejections .

  8. Threshold Optimization Sets Comparison: The paper visually compares the distributions of monitoring scores for different optimization sets (ID, ID+T, ID+O) and the evaluation set. It highlights that the ID+T strategy yields near-optimal thresholds, especially with g-mean, while the ID strategy shows smaller thresholds due to error scores proximity to correct ones. Additionally, the limitations of ID+O are demonstrated, emphasizing the importance of prior knowledge of threats for optimal monitor performance .

  9. Statistical Analysis and Synthesis: The study employs statistical testing, including the Wilcoxon signed-rank tests and the Friedman test, to compare the performance of different threshold optimization approaches across multiple scenarios. This rigorous analysis helps in discerning distinctions between approaches and drawing meaningful conclusions from the experimental results .

  10. Qualitative Discussion and Conclusion: The research concludes that the ID+T approach, leveraging knowledge of anticipated threats, outperforms other strategies. However, it acknowledges the impracticality of assuming prior threat knowledge in safety-critical applications. The study also highlights the potential drawbacks of including generic threat data in the optimization process, suggesting future research directions for tailored monitor design based on narrowly defined threat categories .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of neural network monitoring and threshold optimization. Noteworthy researchers in this field include Avi Arampatzis, André van Hameran, Davide Chicco, Giuseppe Jurman, Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei, Carmen Esposito, Gregory A Landrum, Nadine Schneider, Nikolaus Stiefl, Sereina Riniker, Raul Sena Ferreira, Joris Guérin, Jérémie Guiochet, Helene Waeselynck, among others .

The key to the solution mentioned in the paper involves two distinct solutions to address the challenge of significant class imbalance observed in the optimization sets:

  1. Over-sampling (OS) the minority class in the threshold optimization set to achieve a positive-to-negative ratio between 0.4 and 0.6.
  2. Using another effectiveness measure, g-mean, which is the geometric mean between Recall and Specificity. G-mean is unaffected by class imbalance as it considers samples with negative ground truth, making it a valuable metric for optimization .

How were the experiments in the paper designed?

The experiments in the paper were designed to address specific research questions by conducting extensive experiments in a structured manner . The researchers utilized three image classification datasets (CIFAR10, CIFAR100, SVHN) and two distinct neural network architectures (DenseNet and ResNet) for each dataset . This resulted in a total of 24 monitors evaluated across different scenarios . The experiments aimed to compare different ways of constructing threshold optimization datasets for neural network runtime monitors, focusing on handling unforeseen threats and integrating generic threats to enhance monitor robustness . The study employed statistical testing methods such as the Wilcoxon signed-rank tests and the Friedman test to analyze the performance of different threshold optimization approaches across multiple scenarios .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is comprised of three image classification datasets: CIFAR10, CIFAR100, and SVHN, each paired with two distinct neural network architectures - DenseNet and ResNet . The code for the study is open source and available at the following GitHub repository: https://github.com/jorisguerin/neural-network-monitoring-benchmark .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" provide strong support for the scientific hypotheses that needed to be verified . The study rigorously investigates the effectiveness of monitors in handling unforeseen threats without prior knowledge during threshold adjustments . The experiments explore the robustness of monitors by integrating generic threats into the threshold optimization scheme . The study's methodology includes comparing different threshold optimization approaches across multiple scenarios and evaluation metrics, such as F1, g-mean, Recall, Precision, and Specificity .

The results of the experiments, supported by statistical testing methods like the Wilcoxon signed-rank test, Friedman test, and Nemenyi post-hoc test, reveal significant differences in performance between the four threshold optimization approaches . The study compares the effectiveness measures for threshold tuning, such as over-sampling with F1 (OS+F1) and g-mean, to determine their impact on monitor performance . The findings indicate that different effectiveness measures yield varying results, with OS+F1 generally providing better Recall and F1 scores, while g-mean optimization produces better outcomes in other aspects .

Moreover, the study's results address specific research questions, such as whether monitoring performance can be achieved without assuming prior knowledge of runtime threats during threshold tuning . The findings suggest that certain threshold optimization strategies, like ID+T, can closely mirror the Evaluation set, showcasing the effectiveness of specific approaches in handling unforeseen threats . The study also highlights the importance of selecting appropriate effectiveness measures based on the monitor's objectives, with F1 over-sampling leading to conservative monitors and g-mean encouraging higher system availability .

Overall, the experiments and results presented in the paper offer comprehensive insights and empirical evidence to support the scientific hypotheses related to neural network monitoring and threshold optimization, contributing valuable knowledge to the field of monitor development and performance evaluation .


What are the contributions of this paper?

The paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" makes several key contributions:

  • Investigating the effectiveness of monitors in handling unforeseen threats that are not available during threshold adjustments .
  • Exploring the integration of generic threats into the threshold optimization scheme to enhance the robustness of monitors .
  • Conducting rigorous experiments on various image datasets to evaluate different monitoring techniques and threshold optimization approaches .
  • Comparing different effectiveness measures for threshold tuning, such as g-mean and F1 with and without oversampling, to determine their impact on monitor performance .
  • Highlighting the importance of threshold optimization in transforming monitoring scores into meaningful binary decisions for neural network safety .
  • Providing insights into the implications of different threshold optimization approaches for real-world applications and the performance of monitors .

What work can be continued in depth?

Further research in the field of neural network monitoring can be expanded in several directions based on the existing study:

  • Exploring Threshold Optimization Approaches: Future studies can delve deeper into comparing different threshold optimization strategies, such as the effectiveness of various effectiveness measures like F-score, geometric mean of Recall and Specificity, Matthews correlation coefficient, or Cohen’s kappa .
  • Investigating Threshold Tuning Methods: Research can focus on developing optimized search strategies to efficiently identify the optimal threshold for neural network monitoring systems .
  • Enhancing Robustness with Generic Threats: There is potential for investigating how integrating generic threat data into the threshold optimization process can enhance the robustness of monitors against unforeseen threats .
  • Adapting Methodology to Other Tasks: The experimental methodology used in the study can be adapted to explore other tasks beyond neural network monitoring, such as object detection, to formulate more comprehensive guidelines for crafting robust monitoring systems .
  • Understanding Threat Reaction Variability: It would be beneficial to explore how different families of threats react to the proposed strategies for neural network monitoring, providing insights into the adaptability and effectiveness of monitoring techniques .

Introduction
Background
Importance of safety-critical applications
Current challenges with runtime threat detection
Objective
To evaluate the effectiveness of neural network monitors
Addressing threshold selection for unforeseen threats
Investigating resilience to data distribution shifts
Method
Data Collection
Selection of computer vision datasets
Simulating novel threats (covariate shifts, adversarial attacks)
In-distribution and out-of-distribution threat data
Data Preprocessing
Preparation of training and testing data
Handling imbalanced datasets
Feature extraction and normalization
Threshold Optimization Methods
ID (In-Distribution) - Baseline without threat knowledge
ID+T (In-Distribution with Threats) - Including some threat data
ID+T+O (In-Distribution with Threats and Out-of-Model-Scope) - Comprehensive approach
Comparative analysis of methods
Performance Evaluation
Metrics: F1 score, g-mean, and system availability
In-distribution performance
Out-of-distribution performance under different threat scenarios
Results and Discussion
ID+T and ID+T+O performance comparison
Impact of generic threat inclusion on threshold optimization
Sensitivity to effectiveness measures
Limitations and trade-offs
Future Research Directions
Specific threat categories and their impact
Role of threat knowledge in hyperparameter tuning
Real-world application recommendations
Conclusion
Summary of findings and implications for safety-critical systems
Recommendations for practical implementation of neural network monitors.
Basic info
papers
computer vision and pattern recognition
image and video processing
machine learning
artificial intelligence
Advanced features
Insights
What is the primary focus of the study described in the user input?
How do the researchers address the issue of selecting thresholds for binary decisions in neural network runtime monitors?
What are the four threshold optimization methods compared in the study, and in which setting do they focus?
What are the main findings regarding the performance of threshold optimization methods under in-distribution and out-of-distribution threat data?

Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring

Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet, Joris Guérin·May 14, 2024

Summary

This study investigates the effectiveness of neural network runtime monitors in defending against unforeseen threats in safety-critical applications. It addresses the overlooked issue of selecting appropriate thresholds for binary decisions by examining the resilience of monitors when faced with data distributions different from training data, simulating novel threats, covariate shifts, and adversarial attacks. The research compares four threshold optimization methods for computer vision datasets, focusing on the Out-of-Model-Scope setting, and evaluates their performance using in-distribution and out-of-distribution threat data. The study finds that optimizing thresholds without prior knowledge of runtime threats (ID+T) generally performs well, with the ID+T+O approach showing competitive results. However, including generic threats in optimization can lead to suboptimal thresholds due to the varying nature of the data. The choice of effectiveness measures, such as F1 and g-mean, influences the balance between error detection and system availability. The research suggests that future work should explore more specific threat categories and the role of threat knowledge in hyperparameter tuning for improved performance in real-world scenarios.
Mind map
Comparative analysis of methods
ID+T+O (In-Distribution with Threats and Out-of-Model-Scope) - Comprehensive approach
ID+T (In-Distribution with Threats) - Including some threat data
ID (In-Distribution) - Baseline without threat knowledge
Out-of-distribution performance under different threat scenarios
In-distribution performance
Metrics: F1 score, g-mean, and system availability
Threshold Optimization Methods
In-distribution and out-of-distribution threat data
Simulating novel threats (covariate shifts, adversarial attacks)
Selection of computer vision datasets
Investigating resilience to data distribution shifts
Addressing threshold selection for unforeseen threats
To evaluate the effectiveness of neural network monitors
Current challenges with runtime threat detection
Importance of safety-critical applications
Recommendations for practical implementation of neural network monitors.
Summary of findings and implications for safety-critical systems
Real-world application recommendations
Role of threat knowledge in hyperparameter tuning
Specific threat categories and their impact
Limitations and trade-offs
Sensitivity to effectiveness measures
Impact of generic threat inclusion on threshold optimization
ID+T and ID+T+O performance comparison
Performance Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Research Directions
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Importance of safety-critical applications
Current challenges with runtime threat detection
Objective
To evaluate the effectiveness of neural network monitors
Addressing threshold selection for unforeseen threats
Investigating resilience to data distribution shifts
Method
Data Collection
Selection of computer vision datasets
Simulating novel threats (covariate shifts, adversarial attacks)
In-distribution and out-of-distribution threat data
Data Preprocessing
Preparation of training and testing data
Handling imbalanced datasets
Feature extraction and normalization
Threshold Optimization Methods
ID (In-Distribution) - Baseline without threat knowledge
ID+T (In-Distribution with Threats) - Including some threat data
ID+T+O (In-Distribution with Threats and Out-of-Model-Scope) - Comprehensive approach
Comparative analysis of methods
Performance Evaluation
Metrics: F1 score, g-mean, and system availability
In-distribution performance
Out-of-distribution performance under different threat scenarios
Results and Discussion
ID+T and ID+T+O performance comparison
Impact of generic threat inclusion on threshold optimization
Sensitivity to effectiveness measures
Limitations and trade-offs
Future Research Directions
Specific threat categories and their impact
Role of threat knowledge in hyperparameter tuning
Real-world application recommendations
Conclusion
Summary of findings and implications for safety-critical systems
Recommendations for practical implementation of neural network monitors.
Key findings
13

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of threshold optimization for neural network monitoring to enhance the robustness of monitors in detecting unforeseen threats during inference . This problem is not entirely new but has received little attention in previous studies, which often assume that the runtime data distribution mirrors the training distribution, a strong assumption that may not hold in real-world scenarios where monitors need to safeguard systems against potentially unforeseen threats . The research investigates the effectiveness of monitors in handling unknown threats without prior knowledge during threshold tuning and explores the integration of generic threats into the threshold optimization scheme to improve monitor robustness . By conducting rigorous experiments on various image datasets, the paper seeks to provide insights into these critical aspects of neural network monitoring to ensure the safety and reliability of systems utilizing neural networks .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to the effectiveness of different approaches in building threshold optimization datasets for neural network runtime monitoring. Specifically, the study aims to investigate the effectiveness of these approaches and their implications for real-world applications, focusing on the identification of optimal thresholds for monitors to reject unsafe predictions during inference . The research explores various aspects, including the handling of unforeseen threats, the integration of generic threats into the threshold optimization scheme, and the impact of different families of threats on the proposed strategies .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" proposes several new ideas, methods, and models in the field of neural network monitoring :

  1. Threshold Optimization Methodology: The paper introduces a methodology for threshold optimization in neural network monitoring. It involves constructing optimization sets with data samples related to the target threat (T) and other generic threats (O) to assess the performance of monitoring thresholds when multiple threats are considered .

  2. Experimental Design: The study conducts extensive experiments using three image classification datasets (CIFAR10, CIFAR100, SVHN) and two neural network architectures (DenseNet and ResNet). Four distinct monitoring techniques are implemented, including Mahalanobis, Outside-the-Box, Max Softmax Probability, and Energy methods .

  3. Effectiveness Measures Comparison: The paper compares different effectiveness measures for threshold tuning, such as g-mean and F1 with and without oversampling. It extends the analysis to include F1 without oversampling and evaluates the performance across various evaluation metrics .

  4. Threshold Optimization Sets Comparison: The study visually compares the distributions of monitoring scores for different optimization sets (ID, ID+T, ID+O) and the evaluation set. It analyzes the effectiveness of these approaches and their implications for real-world applications, highlighting the importance of prior knowledge of threats for optimal monitor performance .

  5. Conclusion and Implications: The research affirms that leveraging knowledge of anticipated threats (ID+T approach) leads to optimal thresholds for monitors. However, it acknowledges the impracticality of assuming prior threat knowledge in safety-critical applications. The study also explores the impact of including generic threat data in the optimization process, revealing potential compromises in monitor performance. This suggests a future research direction of integrating data samples from more narrowly defined threat categories for tailored monitor design . The paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" introduces novel approaches and methodologies for neural network monitoring, offering significant advancements over previous methods :

  6. Threshold Optimization Strategies: The study presents four distinct threshold optimization strategies: ID (In-Distribution), ID+T (In-Distribution with Target threat), ID+O (In-Distribution with Other generic threats), and ID+T+O (In-Distribution with Target and Other generic threats). These strategies aim to evaluate the effectiveness of monitors when the target threat is unknown, reflecting a more realistic scenario .

  7. Effectiveness Measures Comparison: The research compares different effectiveness measures, such as F1 with over-sampling and g-mean, to optimize thresholds on the optimization set. The choice of effectiveness measure depends on the monitor's specific objectives, with F1 emphasizing missed errors reduction and g-mean prioritizing system availability by reducing false rejections .

  8. Threshold Optimization Sets Comparison: The paper visually compares the distributions of monitoring scores for different optimization sets (ID, ID+T, ID+O) and the evaluation set. It highlights that the ID+T strategy yields near-optimal thresholds, especially with g-mean, while the ID strategy shows smaller thresholds due to error scores proximity to correct ones. Additionally, the limitations of ID+O are demonstrated, emphasizing the importance of prior knowledge of threats for optimal monitor performance .

  9. Statistical Analysis and Synthesis: The study employs statistical testing, including the Wilcoxon signed-rank tests and the Friedman test, to compare the performance of different threshold optimization approaches across multiple scenarios. This rigorous analysis helps in discerning distinctions between approaches and drawing meaningful conclusions from the experimental results .

  10. Qualitative Discussion and Conclusion: The research concludes that the ID+T approach, leveraging knowledge of anticipated threats, outperforms other strategies. However, it acknowledges the impracticality of assuming prior threat knowledge in safety-critical applications. The study also highlights the potential drawbacks of including generic threat data in the optimization process, suggesting future research directions for tailored monitor design based on narrowly defined threat categories .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of neural network monitoring and threshold optimization. Noteworthy researchers in this field include Avi Arampatzis, André van Hameran, Davide Chicco, Giuseppe Jurman, Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei, Carmen Esposito, Gregory A Landrum, Nadine Schneider, Nikolaus Stiefl, Sereina Riniker, Raul Sena Ferreira, Joris Guérin, Jérémie Guiochet, Helene Waeselynck, among others .

The key to the solution mentioned in the paper involves two distinct solutions to address the challenge of significant class imbalance observed in the optimization sets:

  1. Over-sampling (OS) the minority class in the threshold optimization set to achieve a positive-to-negative ratio between 0.4 and 0.6.
  2. Using another effectiveness measure, g-mean, which is the geometric mean between Recall and Specificity. G-mean is unaffected by class imbalance as it considers samples with negative ground truth, making it a valuable metric for optimization .

How were the experiments in the paper designed?

The experiments in the paper were designed to address specific research questions by conducting extensive experiments in a structured manner . The researchers utilized three image classification datasets (CIFAR10, CIFAR100, SVHN) and two distinct neural network architectures (DenseNet and ResNet) for each dataset . This resulted in a total of 24 monitors evaluated across different scenarios . The experiments aimed to compare different ways of constructing threshold optimization datasets for neural network runtime monitors, focusing on handling unforeseen threats and integrating generic threats to enhance monitor robustness . The study employed statistical testing methods such as the Wilcoxon signed-rank tests and the Friedman test to analyze the performance of different threshold optimization approaches across multiple scenarios .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is comprised of three image classification datasets: CIFAR10, CIFAR100, and SVHN, each paired with two distinct neural network architectures - DenseNet and ResNet . The code for the study is open source and available at the following GitHub repository: https://github.com/jorisguerin/neural-network-monitoring-benchmark .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" provide strong support for the scientific hypotheses that needed to be verified . The study rigorously investigates the effectiveness of monitors in handling unforeseen threats without prior knowledge during threshold adjustments . The experiments explore the robustness of monitors by integrating generic threats into the threshold optimization scheme . The study's methodology includes comparing different threshold optimization approaches across multiple scenarios and evaluation metrics, such as F1, g-mean, Recall, Precision, and Specificity .

The results of the experiments, supported by statistical testing methods like the Wilcoxon signed-rank test, Friedman test, and Nemenyi post-hoc test, reveal significant differences in performance between the four threshold optimization approaches . The study compares the effectiveness measures for threshold tuning, such as over-sampling with F1 (OS+F1) and g-mean, to determine their impact on monitor performance . The findings indicate that different effectiveness measures yield varying results, with OS+F1 generally providing better Recall and F1 scores, while g-mean optimization produces better outcomes in other aspects .

Moreover, the study's results address specific research questions, such as whether monitoring performance can be achieved without assuming prior knowledge of runtime threats during threshold tuning . The findings suggest that certain threshold optimization strategies, like ID+T, can closely mirror the Evaluation set, showcasing the effectiveness of specific approaches in handling unforeseen threats . The study also highlights the importance of selecting appropriate effectiveness measures based on the monitor's objectives, with F1 over-sampling leading to conservative monitors and g-mean encouraging higher system availability .

Overall, the experiments and results presented in the paper offer comprehensive insights and empirical evidence to support the scientific hypotheses related to neural network monitoring and threshold optimization, contributing valuable knowledge to the field of monitor development and performance evaluation .


What are the contributions of this paper?

The paper "Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring" makes several key contributions:

  • Investigating the effectiveness of monitors in handling unforeseen threats that are not available during threshold adjustments .
  • Exploring the integration of generic threats into the threshold optimization scheme to enhance the robustness of monitors .
  • Conducting rigorous experiments on various image datasets to evaluate different monitoring techniques and threshold optimization approaches .
  • Comparing different effectiveness measures for threshold tuning, such as g-mean and F1 with and without oversampling, to determine their impact on monitor performance .
  • Highlighting the importance of threshold optimization in transforming monitoring scores into meaningful binary decisions for neural network safety .
  • Providing insights into the implications of different threshold optimization approaches for real-world applications and the performance of monitors .

What work can be continued in depth?

Further research in the field of neural network monitoring can be expanded in several directions based on the existing study:

  • Exploring Threshold Optimization Approaches: Future studies can delve deeper into comparing different threshold optimization strategies, such as the effectiveness of various effectiveness measures like F-score, geometric mean of Recall and Specificity, Matthews correlation coefficient, or Cohen’s kappa .
  • Investigating Threshold Tuning Methods: Research can focus on developing optimized search strategies to efficiently identify the optimal threshold for neural network monitoring systems .
  • Enhancing Robustness with Generic Threats: There is potential for investigating how integrating generic threat data into the threshold optimization process can enhance the robustness of monitors against unforeseen threats .
  • Adapting Methodology to Other Tasks: The experimental methodology used in the study can be adapted to explore other tasks beyond neural network monitoring, such as object detection, to formulate more comprehensive guidelines for crafting robust monitoring systems .
  • Understanding Threat Reaction Variability: It would be beneficial to explore how different families of threats react to the proposed strategies for neural network monitoring, providing insights into the adaptability and effectiveness of monitoring techniques .
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