Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for ...

Louise Matsakis covers cybersecurity, internet law, and online culture for WIRED. Now, a leading group of researchers from MIT have found a different answer, in a paper that was presented earlier this ...

Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns about the model robustness. Existing methods are mainly ...

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 ...

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 ...

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 ...