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 ...

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 ...

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, ...

Accuracies obtained by the most effective configuration of each of the seven different attacks across the three datasets. The Jacobian-based Saliency Map Attack (JSMA) was the most effective in ...

Imagine the following scenarios: An explosive device, an enemy fighter jet and a group of rebels are misidentified as a cardboard box, an eagle or a sheep herd. A lethal autonomous weapons system ...

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 ...

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 ...

Hackers and evildoers are using adversarial poetry to jailbreak AI. The trick involves writing poems as prompts. AI ...

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 ...