Adversarial attacks are not something new to the world of Deep Networks used for image recognition. However, as the research with Deep Learning grows, more flaws are uncovered. The team at the University of KU Leuven in Belgium have demonstrated how, by simple using a colored photo held near the torso of a man can render him invisible to image recognition systems based on convolutional neural networks.
Convolutional Neural Networks or CNNs are a class of Deep learning networks that reduces the number of computations to be performed by creating hierarchical patterns from simpler and smaller networks. They are becoming the norm for image recognition applications and are being used in the field. In this new paper, the addition of color patches is seen to confuse the image detector YoLo(v2) by adding noise that disrupts the calculations of the CNN. The patch is not random and can be identified using the process defined in the publication.
This attack can be implemented by printing the disruptive pattern on a t-shirt making them invisible to surveillance system detection. You can read the paper[PDF] that outlines the generation of the adversarial patch. Image recognition camouflage that works on Google’s Inception has been documented in the past and we hope to see more such hacks in the future. Its a new world out there where you hacking is colorful as ever.
Continue reading “The Cloak Of Invisibility Against Image Recognition”
We salute hackers who make technology useful for people in emerging markets. Leigh Johnson joined that select group when she accepted the challenge to build portable machine vision units that work offline and can be deployed for under $100 each. For hardware, a Raspberry Pi with camera plus screen can fit under that cost ceiling, and the software to give it sight is the focus of her 2018 Hackaday Superconference presentation. (Video also embedded below.)
The talk is a very concise 13 minutes, so Leigh flies through definitions of basic terms, before quickly naming TensorFlow and Keras as the tools she used. The time she saved here was spent on explaining what convolutional neural networks are and how they work, just enough to prepare the audience. But all of that is really just background, the meat of the talk is self-contained examples that Leigh has put together and made available online. I love to see that since it means you go beyond just watching and try it out for yourself. Continue reading “Leigh Johnson’s Guide To Machine Vision On Raspberry Pi”
If you live in a bustling city and have anyone over who drives, it can be difficult for them to find parking. Maybe you have an assigned space, but they’re resigned to circling the block with an eagle eye. With those friends in mind, [Adam Geitgey] wrote a Python script that takes the video feed from a web cam and analyzes it frame by frame to figure out when a street parking space opens up. When the glorious moment arrives, he gets a text message via Twilio with a picture of the void.
It sounds complicated, but much of the work has already been done. Cars are a popular target for machine learning, so large data sets with cars already exist. [Adam] didn’t have to train a neural network, either–he found a pre-trained Mask R-CNN model with data for 80 common objects like people, animals, and cars.
The model gives a lot of useful info, including a bounding box for each car with pixel coordinates. Since the boxes overlap, there needs be a way to determine whether there’s really a car in the space, or just the bumpers of other cars. [Adam] used intersection over union to do this, which is conveniently available as a function of the Mask R-CNN model’s library. The function returns a score, so it was just a matter of ignoring low-scoring bounding boxes.
[Adam] purposely made the script adaptable. A few changes here and there, and you could be picking up tennis balls with a robotic collector or analyzing human migration patterns on your block in no time. Or change it up and detect all the cars that run the stop sign by your house.
Thanks for the tip, [foamyguy].