A python library that aims to simulate failures that may occur in a camera during the acquisition/processing phase.

To support the definition of safe and robust vehicle architectures and intelligent systems, we define the failures model of a vehicle camera, together with an analysis of effects and known mitigations. As a natural consequence, here we present a software library for the generation of the corresponding failed images. These images are then fed the trained agent of an autonomous driving simulator: the misbehavior of the trained agent allows a better understanding of failures effects and especially of the resulting safety risk. 

Some examples of the failures injected:

Github is available at: https://github.com/francescosecci/Python_Image_Failures

More information:

• Thesis of Francesco Secci, "On failures of RGB cameras and their effects in autonomous driving applications", Master Thesis at the University of Florence, Italy (in Italian only), July 2020. Supervisor: Andrea Ceccarelli (link to thesis page)
• Paper: Francesco Secci, Andrea Ceccarelli, "On failures of RGB cameras and their effects in autonomous driving applications", in press -- to appear at the 31st International Symposium on Software Reliability Engineering (ISSRE 2020), available on arXiv

The tool is a custom version of the LearningByCheating autonomous driving agent and related suite, which has been integrated with the IBM Adversarial Robustness Toolbox (ART) for the injection of 4 attacks on the RGB camera:

• Spatial Transformation (STA),
• HopSkipJump (HSJ),
• Basic Iterative Method (BIM),
• NewtonFool (NF).

RELOAD (Rapid EvaLuation Of Anomaly Detectors) is a tool that allows to easily compare different algorithms for anomaly detection. It is written in Java, wrapping algorithms coming from other Java-based frameworks such as ELKI or WEKA.

For further information, please refer to the Github WIKI.

Information about the tool can be found in the following papers:

• Zoppi, T., Ceccarelli, A., & Bondavalli, A. (2019). Evaluation of Anomaly Detectors Made Easy with RELOAD. To appear at 30th International Symposium on Software Reliability Engineering (ISSRE 2019), Oct 2019.
• Zoppi, T., Ceccarelli, A., & Bondavalli, A. (2019). MADneSs: a Multi-layer Anomaly Detection Framework for Complex Dynamic Systems. IEEE Transactions on Dependable and Secure Computing, DOI: 10.1109/TDSC.2019.2908366 (2019, May)
• Falcão, F., Zoppi, T., Silva, C. B. V., Santos, A., Fonseca, B., Ceccarelli, A., & Bondavalli, A. (2019, April). Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 318-327), ACM. DOI: 10.1145/3297280.3297314

The CHESS "State-Based Analysis" plugin is part of PolarSys CHESS, an open source methodology and tool for the development of high-integrity embedded systems. The CHESS methodology was devised and implemented initially in the CHESS project, later extended in the CONCERTO project, and then further developed within other projects.

This plugin performs Quantitative Dependability Analysis using a variant of the Stochastic Petri Nets formalism, starting from models specified in the CHESS ML language. The plugin is able to automatically compute system-level dependability metrics, based on dependability properties of individual components, and a description of the system, software, and/or hardware architecture.  

The whole CHESS Framework is released as open source and it is now an Eclipse project under the PolarSys Working Group. CHESS-SBA is also available on GitHub, jointly with an extensive wiki as documentation.

For more information please contact Leonardo Montecchi or Paolo Lollini.