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 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, ...

Most artificial intelligence researchers agree that one of the key concerns of machine learning is adversarial attacks, data manipulation techniques that cause trained models to behave in undesired ...

Artificial intelligence has achieved dramatic success over the past decade, with the triumph in predicting protein structures marked as the latest milestone. At the same time, quantum computing has ...

Artificial intelligence and machine learning (AI/ML) systems trained using real-world data are increasingly being seen as open to certain attacks that fool the systems by using unexpected inputs. At ...

Adversarial AI, ChatGPT-powered social engineering, and paid advertising attacks are among the most dangerous emerging attack methods, according to SANS Institute analysts. Cyber experts from the SANS ...

IFAP generates adversarial perturbations using model gradients and then shapes them in the discrete cosine transform (DCT) domain. Unlike existing frequency-aware methods that apply a fixed frequency ...

Artificial intelligence (AI) models that evaluate medical images have potential to speed up and improve accuracy of cancer diagnoses, but they may also be vulnerable to cyberattacks. In a new study, ...

HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma Adversarial images represent a ...