Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...

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 ...

The National Institute of Standards and Technology (NIST) has published its final report on adversarial machine learning (AML), offering a comprehensive taxonomy and shared terminology to help ...

The final guidance for defending against adversarial machine learning offers specific solutions for different attacks, but warns current mitigation is still developing. NIST Cyber Defense The final ...

The study analyzed 121 short videos as part of a small dataset to distinguish between truthful and deceptive conversations. Scientists have revealed that Convolutional Neural Networks (CNNs), a type ...

Scientists have revealed that Convolutional Neural Networks (CNNs), a type of deep learning algorithm, demonstrate superior performance compared to conventional non-machine learning approaches when ...

The study analyzed 121 short videos as part of a small dataset to distinguish between truthful and deceptive conversations. Credit: Expert Systems with Applications (2025). DOI: The research examined ...