Abstract Robustness becomes one of the most desired properties in machine learning (ML) models due to their increasing adoption in safety/security-sensitive settings. Most attempts to train robust methods…
Abstract We aim to build neural networks that are intrinsically robust against adversarial attacks. We focus on classifying images in real-world scenarios with complex backgrounds under unforeseen adversarial…
Abstract Although deep neural networks (DNNs) have achieved impressive performance in several applications, they also exhibit several well-known sensitivities and security concerns that can emerge for a variety…
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Abstract Current machine learning models suffer from evasion attacks such as adversarial examples. This introduces security and safety concerns that lack any clear solution. Recently, the usage of…
Machine learning provides valuable methodologies for detecting and protecting against security attacks at scale. However, a machine-learning algorithm used for security is different from other domains because in…
Our research is about providing safe access to the Internet in places where network access is restricted or censored. Many people are limited in what they can say…
CLTC has launched a new “publication” on Medium, the popular blogging platform. Designed to be a more informal outlet for our communications, the CLTC Bulletin features news, research and opinions from CLTC staff and researchers. Check it out at https:///medium.com/cltc-bulletin.
South Korea is home to one of the world’s most robust digital infrastructures, largely as a result of a government-led development model that has favored domestic technologies.…
