Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing

• URL: http://arxiv.org/abs/2402.00035v3
• Date: Fri, 28 Jun 2024 15:42:12 GMT
• Title: Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
• Authors: Yizhak Elboher, Raya Elsaleh, Omri Isac, Mélanie Ducoffe, Audrey Galametz, Guillaume Povéda, Ryma Boumazouza, Noémie Cohen, Guy Katz,
• Abstract summary: Deep neural networks (DNNs) are becoming the prominent solution for many computational problems. In this case-study paper, we demonstrate the robustness of an image-classifier DNN intended for use during the aircraft taxiing phase. We use formal methods to assess this DNN's robustness to three common image perturbation types: noise, brightness and contrast, and some of their combinations.
• Score: 1.1454187767262163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot workload and in improving operational safety. However, the use of DNNs in this type of safety-critical applications requires a thorough certification process. This need can be addressed through formal verification, which provides rigorous assurances -- e.g.,~by proving the absence of certain mispredictions. In this case-study paper, we demonstrate this process using an image-classifier DNN currently under development at Airbus and intended for use during the aircraft taxiing phase. We use formal methods to assess this DNN's robustness to three common image perturbation types: noise, brightness and contrast, and some of their combinations. This process entails multiple invocations of the underlying verifier, which might be computationally expensive; and we therefore propose a method that leverages the monotonicity of these robustness properties, as well as the results of past verification queries, in order to reduce the overall number of verification queries required by nearly 60%. Our results provide an indication of the level of robustness achieved by the DNN classifier under study, and indicate that it is considerably more vulnerable to noise than to brightness or contrast perturbations.

Related papers

• STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario [50.37501379058119]
  We propose the Spatial Transform Black-box Attack (STBA) to craft formidable adversarial examples in the query-limited scenario. We show that STBA could effectively improve the imperceptibility of the adversarial examples and remarkably boost the attack success rate under query-limited settings.
  arXiv  Detail & Related papers  (2024-03-30T13:28:53Z)
• DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace [2.624902795082451]
  Software development in the aerospace domain requires adhering to strict, high-quality standards. It is unclear how to allow aerospace systems to benefit from the deep learning revolution.
  arXiv  Detail & Related papers  (2024-01-04T14:01:24Z)
• Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing [57.49021927832259]
  Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios. However, their intricate designs and lack of transparency raise safety concerns when applied in real-world applications. Formal Verification (FV) of DNNs has emerged as a valuable solution to provide provable guarantees on the safety aspect.
  arXiv  Detail & Related papers  (2023-12-10T13:51:25Z)
• Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly [79.07074710460012]
  adversarial vulnerability of deep neural networks (DNNs) has drawn great attention. An increasing number of transfer-based methods have been developed to fool black-box DNN models. We establish a transfer-based attack benchmark (TA-Bench) which implements 30+ methods.
  arXiv  Detail & Related papers  (2023-11-02T15:35:58Z)
• Visual inspection for illicit items in X-ray images using Deep Learning [7.350725076596881]
  Automated detection of contraband items in X-ray images can significantly increase public safety. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task.
  arXiv  Detail & Related papers  (2023-10-05T16:35:27Z)
• The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks [94.63547069706459]
  #DNN-Verification problem involves counting the number of input configurations of a DNN that result in a violation of a safety property. We propose a novel approach that returns the exact count of violations. We present experimental results on a set of safety-critical benchmarks.
  arXiv  Detail & Related papers  (2023-01-17T18:32:01Z)
• Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection [67.49587673594276]
  We introduce a new perceptron bias assumption that suggests discriminator models are more sensitive to certain features of the input, leading to the overconfidence problem. We demonstrate that the diffusion denoising process (DDP) of DMs serves as a novel form of asymmetric, which is well-suited to enhance the input and mitigate the overconfidence problem. Our experiments on CIFAR10, CIFAR100, and ImageNet show that our method outperforms SOTA approaches.
  arXiv  Detail & Related papers  (2022-11-21T08:45:08Z)
• DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers [16.893762648621266]
  We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations.
  arXiv  Detail & Related papers  (2021-03-02T10:41:16Z)
• A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications [0.0]
  Key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. Different methods for uncertainty quantification have recently been proposed to measure the inevitable source of errors in data and models. These methods require a higher computational load, a higher memory footprint, and introduce extra latency, which can be prohibitive in safety-critical applications.
  arXiv  Detail & Related papers  (2020-06-26T18:55:10Z)
• GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples [77.99182201815763]
  Deep neural networks (DNNs) are vulnerable to adversarial examples and other data perturbations. GraN is a time- and parameter-efficient method that is easily adaptable to any DNN. GraN achieves state-of-the-art performance on numerous problem set-ups.
  arXiv  Detail & Related papers  (2020-04-20T10:09:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.