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Real Faults in Deep Learning Fault Benchmarks: How Real Are They?
Gunel Jahangirova, Nargiz Humbatova, Jinhan Kim +2
As the adoption of Deep Learning (DL) systems continues to rise, an increasing number of approaches are being proposed to test these systems, localise faults within them, and repai…
An Empirical Study of Fault Localisation Techniques for Deep Learning
Nargiz Humbatova, Jinhan Kim, Gunel Jahangirova +2
With the increased popularity of Deep Neural Networks (DNNs), increases also the need for tools to assist developers in the DNN implementation, testing and debugging process. Sever…
A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles
Masoud Jamshidiyan Tehrani, Jinhan Kim, Rosmael Zidane Lekeufack Foulefack +2
The advent of deep learning and its astonishing performance has enabled its usage in complex systems, including autonomous vehicles. On the other hand, deep learning models are sus…
Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs
Samuele Pasini, Jinhan Kim, Tommaso Aiello +3
Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements. A key challenge is…