An adversarial preturbation is a small, human-imperceptible change to a piece of data that flummoxes an otherwise well-behaved machine learning classifier: for example, there's a really accurate ML model that guesses which full-sized image corresponds to a small thumbnail, but if you change just one pixel in the thumbnail, the classifier stops working almost entirely. Read the rest

Researchers from KU Leuven have published a paper showing how they can create a 40cm x 40cm "patch" that fools a convoluted neural network classifier that is otherwise a good tool for identifying humans into thinking that a person is not a person -- something that could be used to defeat AI-based security camera systems. They theorize that the could just print the patch on a t-shirt and get the same result. Read the rest

Researchers from Tencent Keen Security Lab have published a report detailing their successful attacks on Tesla firmware, including remote control over the steering, and an adversarial example attack on the autopilot that confuses the car into driving into the oncoming traffic lane. Read the rest

For several years, I've been covering the bizarre phenomenon of "adversarial examples (AKA "adversarial preturbations"), these being often tiny changes to data than can cause machine-learning classifiers to totally misfire: imperceptible squeaks that make speech-to-text systems hallucinate phantom voices; or tiny shifts to a 3D image of a helicopter that makes image-classifiers hallucinate a rifle Read the rest

Yesterday, despite the manifest, glaring problems with its porn filter, Tumblr turned on mandatory porn-blocking for all its users' content, so that anything that its bots identified a pornographic would be invisible. Read the rest

Last year, Princeton researchers revealed a powerful new ad-blocking technique: perceptual ad-blocking uses a machine-learning model trained on images of pages with the ads identified to make predictions about which page elements are ads to block and which parts are not. Read the rest

In All You Need is “Love”: Evading Hate Speech Detection, a Finnish-Italian computer science research team describe their research on evading hate-speech detection algorithms; their work will be presented next month in Toronto at the ACM Workshop on Artificial Intelligence and Security. Read the rest

Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. Read the rest

Robot law pioneer Ryan Calo (previously) teamed up with U Washington computer science and law-school colleagues to write Is Tricking a Robot Hacking? -- a University of Washington School of Law Research Paper. Read the rest

A group of Chinese computer scientists from academia and industry have published a paper documenting a tool for fooling facial recognition software by shining hat-brim-mounted infrared LEDs on the user's face, projecting CCTV-visible, human-eye-invisible shapes designed to fool the face recognition software. Read the rest

Many people worry that 3D printers will usher in an epidemic of untraceable "ghost guns," particularly guns that might evade some notional future gun control regime that emerges out of the current movement to put sensible, minimal curbs on guns, particularly anti-personnel guns. Read the rest

Machine learning models use statistical analysis of historical data to predict future events: whether you are a good candidate for a loan, whether you will violate parole, or whether the thing in the road ahead is a stop sign or a moose. Read the rest

Google's Cloud Automl Vision system -- a machine-learning-based image classifier -- is now available to the general public; anyone can sign up to the program, upload a set of 20-10,000 images and train a new model with them, which they can then use. Read the rest

In Partial Information Attacks on Real-world AI, a group of MIT computer science researchers report on their continuing work fooling Google's image-classifier, this time without any knowledge of how the classifier works. Read the rest