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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …
in achieving human-level performance on several long-standing tasks. With the broader …
Assuring the machine learning lifecycle: Desiderata, methods, and challenges
Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …
successful applications. The potential for this success to continue and accelerate has placed …
Software engineering for AI-based systems: a survey
AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
Machine learning testing: Survey, landscapes and horizons
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
Deephunter: a coverage-guided fuzz testing framework for deep neural networks
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Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …
one or more components that learn how to perform a task from a given data set. The …
Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward
Connected and autonomous vehicles (CAVs) will form the backbone of future next-
generation intelligent transportation systems (ITS) providing travel comfort, road safety …
generation intelligent transportation systems (ITS) providing travel comfort, road safety …
Testing deep neural networks
Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …
them must be thoroughly tested, especially in safety-critical domains. However, traditional …
Deepxplore: Automated whitebox testing of deep learning systems
Deep learning (DL) systems are increasingly deployed in safety-and security-critical
domains including self-driving cars and malware detection, where the correctness and …
domains including self-driving cars and malware detection, where the correctness and …