Emese Thamó | University of Liverpool (original) (raw)

Papers by Emese Thamó

Research paper thumbnail of A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

Computer Science Review, Aug 1, 2020

In the past few years, significant progress has been made on deep neural networks (DNNs) in achie... more 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 deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these concerns is particularly active, with a significant number of papers released in the past few years. This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017. Contents * This work is supported by the UK EPSRC projects on Offshore Robotics for Certification of Assets (ORCA) [EP/R026173/1] and End-to-End Conceptual Guarding of Neural Architectures [EP/T026995/1], and ORCA Partnership Resource Fund (PRF) on Towards the Accountable and Explainable Learning-enabled Autonomous Robotic Systems, as well as the UK Dstl projects on Test Coverage Metrics for Artificial Intelligence.

Research paper thumbnail of A Survey of Safety and Trustworthiness of Deep Neural Networks

arXiv (Cornell University), Dec 18, 2018

In the past few years, significant progress has been made on deep neural networks (DNNs) in achie... more 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 deployment of DNNs on various applications, the concerns on its safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents of self-driving cars. Research to address these concerns is very active, with many papers released in the past few years. This survey paper conducts a review of the current research effort on making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we surveyed 178 papers, most of which published after 2017.

Research paper thumbnail of Safety and Trustworthiness of Deep Neural Networks: A Survey

arXiv (Cornell University), Dec 18, 2018

Research paper thumbnail of A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability

arXiv (Cornell University), Dec 17, 2018

In the past few years, significant progress has been made on deep neural networks (DNNs) in achie... more 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 deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these concerns is particularly active, with a significant number of papers released in the past few years. This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017. Contents * This work is supported by the UK EPSRC projects on Offshore Robotics for Certification of Assets (ORCA) [EP/R026173/1] and End-to-End Conceptual Guarding of Neural Architectures [EP/T026995/1], and ORCA Partnership Resource Fund (PRF) on Towards the Accountable and Explainable Learning-enabled Autonomous Robotic Systems, as well as the UK Dstl projects on Test Coverage Metrics for Artificial Intelligence.

Research paper thumbnail of A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

Computer Science Review, Aug 1, 2020

In the past few years, significant progress has been made on deep neural networks (DNNs) in achie... more 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 deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these concerns is particularly active, with a significant number of papers released in the past few years. This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017. Contents * This work is supported by the UK EPSRC projects on Offshore Robotics for Certification of Assets (ORCA) [EP/R026173/1] and End-to-End Conceptual Guarding of Neural Architectures [EP/T026995/1], and ORCA Partnership Resource Fund (PRF) on Towards the Accountable and Explainable Learning-enabled Autonomous Robotic Systems, as well as the UK Dstl projects on Test Coverage Metrics for Artificial Intelligence.

Research paper thumbnail of A Survey of Safety and Trustworthiness of Deep Neural Networks

arXiv (Cornell University), Dec 18, 2018

In the past few years, significant progress has been made on deep neural networks (DNNs) in achie... more 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 deployment of DNNs on various applications, the concerns on its safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents of self-driving cars. Research to address these concerns is very active, with many papers released in the past few years. This survey paper conducts a review of the current research effort on making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we surveyed 178 papers, most of which published after 2017.

Research paper thumbnail of Safety and Trustworthiness of Deep Neural Networks: A Survey

arXiv (Cornell University), Dec 18, 2018

Research paper thumbnail of A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability

arXiv (Cornell University), Dec 17, 2018

In the past few years, significant progress has been made on deep neural networks (DNNs) in achie... more 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 deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these concerns is particularly active, with a significant number of papers released in the past few years. This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017. Contents * This work is supported by the UK EPSRC projects on Offshore Robotics for Certification of Assets (ORCA) [EP/R026173/1] and End-to-End Conceptual Guarding of Neural Architectures [EP/T026995/1], and ORCA Partnership Resource Fund (PRF) on Towards the Accountable and Explainable Learning-enabled Autonomous Robotic Systems, as well as the UK Dstl projects on Test Coverage Metrics for Artificial Intelligence.