The field of adversarial attacks in natural language processing (NLP) concerns the deliberate introduction of subtle perturbations into textual inputs with the aim of misleading deep learning models, ...

Deep neural networks (DNNs) have become a cornerstone of modern AI technology, driving a thriving field of research in ...

New vulnerabilities have emerged with the rapid advancement and adoption of multimodal foundational AI models, significantly expanding the potential for cybersecurity attacks. Researchers at Los ...

Adversarial AI exploits model vulnerabilities by subtly altering inputs (like images or code) to trick AI systems into misclassifying or misbehaving. These attacks often evade detection because they ...

The integration of deep learning techniques into wireless communication systems has catalysed notable advancements in tasks such as modulation classification and spectrum sensing. However, the ...

We are witnessing a rapid advancement of AI and its impact across various industries. However, with great power comes great responsibility, and one of the emerging challenges in the AI landscape is ...

Machines' ability to learn by processing data gleaned from sensors underlies automated vehicles, medical devices and a host of other emerging technologies. But that learning ability leaves systems ...

Adversarial attacks against the technique that powers game-playing AIs and could control self-driving cars shows it may be less robust than we thought. The soccer bot lines up to take a shot at the ...

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