Invisible mask: practical attacks on face recognition with infrared

Invisible mask: practical attacks on face recognition with infrared Zhou et al., arXiv’18 You might have seen selected write-ups from The Morning Paper appearing in ACM Queue. The editorial board there are also kind enough to send me paper recommendations when they come across something that sparks their interest. So this week things are going … Continue reading Invisible mask: practical attacks on face recognition with infrared

Learning a unified embedding for visual search at Pinterest

Learning a unified embedding for visual search at Pinterest Zhai et al., KDD'19 Last time out we looked at some great lessons from Airbnb as they introduced deep learning into their search system. Today’s paper choice highlights an organisation that has been deploying multiple deep learning models in search (visual search) for a while: Pinterest. … Continue reading Learning a unified embedding for visual search at Pinterest

Applying deep learning to Airbnb search

Applying deep learning to Airbnb search Haldar et al., KDD'19 Last time out we looked at Booking.com’s lessons learned from introducing machine learning to their product stack. Today’s paper takes a look at what happened in Airbnb when they moved from standard machine learning approaches to deep learning. It’s written in a very approachable style … Continue reading Applying deep learning to Airbnb search

150 successful machine learning models: 6 lessons learned at Booking.com

150 successful machine learning models: 6 lessons learned at Booking.com Bernadi et al., KDD'19 Here’s a paper that will reward careful study for many organisations. We’ve previously looked at the deep penetration of machine learning models in the product stacks of leading companies, and also some of the pre-requisites for being successful with it. Today’s … Continue reading 150 successful machine learning models: 6 lessons learned at Booking.com

Detecting and characterizing lateral phishing at scale

Detecting and characterizing lateral phishing at scale Ho et al., USENIX Security Symposium 2019 This is an investigation into the phenomenon of lateral phishing attacks. A lateral phishing attack is one where a compromised account within an organisation is used to send out further phishing emails (typically to other employees within the same organisation). So … Continue reading Detecting and characterizing lateral phishing at scale

In-toto: providing farm-to-table guarantees for bits and bytes

in-toto: providing farm-to-table guarantees for bits and bytes Torres-Arias et al., USENIX Security Symposium 2019 Small world with high risks did a great job of highlighting the absurd risks we’re currently carrying in many software supply chains. There are glimmers of hope though. This paper describes in-toto, and end-to-end system for ensuring the integrity of … Continue reading In-toto: providing farm-to-table guarantees for bits and bytes