Collaborate Disseminate
I just co-authored a paper on the legal risks of doing machine learning research, given the current state of the Computer Fraud and Abuse Act: Abstract: Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML sy… Continue reading Adversarial Machine Learning and the CFAA
I just co-authored a paper on the legal risks of doing machine learning research, given the current state of the Computer Fraud and Abuse Act: Abstract: Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities. In this paper, we ask, "What are the… Continue reading Adversarial Machine Learning and the CFAA
Fawkes is a system for manipulating digital images so that they aren’t recognized by facial recognition systems. At a high level, Fawkes takes your personal images, and makes tiny, pixel-level changes to them that are invisible to the human eye, in a process we call image cloaking. You can then use these "cloaked" photos as you normally would, sharing them… Continue reading Fawkes: Digital Image Cloaking
Interesting story of how the police can identify someone by following the evidence chain from website to website. According to filings in Blumenthal’s case, FBI agents had little more to go on when they started their investigation than the news helicopter footage of the woman setting the police car ablaze as it was broadcast live May 30. It showed the… Continue reading Identifying a Person Based on a Photo, LinkedIn and Etsy Profiles, and Other Internet Bread Crumbs
Interesting story of how the police can identify someone by following the evidence chain from website to website. According to filings in Blumenthal’s case, FBI agents had little more to go on when they started their investigation than the news helicop… Continue reading Identifying a Person Based on a Photo, LinkedIn and Etsy Profiles, and Other Internet Bread Crumbs
New research on using specially crafted inputs to slow down machine-learning neural network systems: Sponge Examples: Energy-Latency Attacks on Neural Networks shows how to find adversarial examples that cause a DNN to burn more energy, take more time, or both. They affect a wide range of DNN applications, from image recognition to natural language processing (NLP). Adversaries might use these… Continue reading Availability Attacks against Neural Networks
Note that this is "announced," so we don’t know when it’s actually going to be implemented. Facebook today announced new features for Messenger that will alert you when messages appear to come from financial scammers or potential child abusers,… Continue reading Facebook Announces Messenger Security Features that Don’t Compromise Privacy
Note that this is "announced," so we don’t know when it’s actually going to be implemented. Facebook today announced new features for Messenger that will alert you when messages appear to come from financial scammers or potential child abusers, displaying warnings in the Messenger app that provide tips and suggest you block the offenders. The feature, which Facebook started rolling… Continue reading Facebook Announces Messenger Security Features that Don’t Compromise Privacy
MIT researchers have built a system that fools natural-language processing systems by swapping words with synonyms: The software, developed by a team at MIT, looks for the words in a sentence that are most important to an NLP classifier and replaces them with a synonym that a human would find natural. For example, changing the sentence "The characters, cast in… Continue reading Fooling NLP Systems Through Word Swapping
Microsoft is training a machine-learning system to find software bugs: At Microsoft, 47,000 developers generate nearly 30 thousand bugs a month. These items get stored across over 100 AzureDevOps and GitHub repositories. To better label and prioritize … Continue reading Vulnerability Finding Using Machine Learning