Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …

At the present time, machine learning methods have been becoming popular and the usage
areas of these methods have also increased with this popularity. The machine learning …

Most of the data manipulation attacks on deep neural networks (DNNs) during the training
stage introduce a perceptible noise that can be catered by preprocessing during inference …

The tremendous growth of interconnectivity and dependencies of physical and cyber
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …

Deep learning models have achieved an impressive performance in a variety of tasks, but
they often suffer from overfitting and are vulnerable to adversarial attacks. Previous research …

Deep learning has achieved remarkable success in a wide range of computer vision tasks.
However, recent researches suggest that deep learning systems are vulnerable to a variety …

While deep learning-based sequential recommender systems are widely used in practice,
their sensitivity to untargeted training data perturbations is unknown. Untargeted …

Recent focus on robustness to adversarial attacks for deep neural networks produced a
large variety of algorithms for training robust models. Most of the effective algorithms involve …

The advancement in deep learning has taken the technology world by storm in the last
decade. Although, there is enormous progress made in terms of algorithm performance, the …

Not too long ago, AI security used to mean the research and practice of how AI can empower
cybersecurity, that is, AI for security. Ever since Ian Goodfellow and his team popularized …