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”
One of the things that makes us human is our ability to communicate. However, a stroke or other medical impairment can take that ability away without warning. Although Stephen Hawking managed to do great things with a computer-aided voice, it took a lot of patience and technology to get there. Composing an e-mail or an utterance for a speech synthesizer using a tongue stick or by blinking can be quite frustrating since most people can only manage about ten words a minute. Conventional speech averages about 150 words per minute. However, scientists recently reported in the journal Nature that they have successfully decoded brain signals into speech directly, which could open up an entirely new world for people who need assistance communicating.
The tech is still only lab-ready, but they claim to be able to produce mostly intelligible sentences using the technique. Previous efforts have only managed to produce single syllables, not entire sentences.
Continue reading “Scientists Create Speech From Brain Signals”
TensorFlow is a popular machine learning package, that among other things, is particularly adept at image recognition. If you want to use a webcam to monitor cats on your lawn or alert you to visitors, TensorFlow can help you achieve this with a bunch of pre-baked libraries. [Eric] took a different tack with PrintRite – using TensorFlow to monitor his 3D printer and warn him of prints gone bad – or worse.
The project relies on training TensorFlow to recognize images of 3D prints gone bad. If layers are separated, or the nozzle is covered in melted goo, it’s probably a good idea to stop the print. Worst case, your printer could begin smoking or catch fire – in that case, [Eric] has the system configured to shut the printer off using a TP-Link Wi-Fi enabled power socket.
Currently, the project exists as a plugin for OctoPrint and relies on two Raspberry Pis – a Zero to handle the camera, and a 3B+to handle OctoPrint and the TensorFlow software. It’s in an early stage of development and is likely not quite ready to replace human supervision. Still, this is a project that holds a lot of promise, and we’re eager to see further development in this area.
There’s a lot of development happening to improve the reliability of 3D printers – we’ve even seen a trick device for resuming failed prints.
Most people are familiar with the idea that machine learning can be used to detect things like objects or people, but for anyone who’s not clear on how that process actually works should check out [Kurokesu]’s example project for detecting pedestrians. It goes into detail on exactly what software is used, how it is configured, and how to train with a dataset.
The application uses a USB camera and the back end work is done with Darknet, which is an open source framework for neural networks. Running on that framework is the YOLO (You Only Look Once) real-time object detection system. To get useful results, the system must be trained on large amounts of sample data. [Kurokesu] explains that while pre-trained networks can be used, it is still necessary to fine-tune the system by adding a dataset which more closely models the intended application. Training is itself a bit of a balancing act. A system that has been overly trained on a model dataset (or trained on too small of a dataset) will suffer from overfitting, a condition in which the system ends up being too picky and unable to usefully generalize. In terms of pedestrian detection, this results in false negatives — pedestrians that don’t get flagged because the system has too strict of an idea about what a pedestrian should look like.
[Kurokesu]’s walkthrough on pedestrian detection is great, but for those interested in taking a step further back and rolling their own projects, this fork of Darknet contains YOLO for Linux and Windows and includes practical notes and guides on installing, using, and training from a more general perspective. Interested in learning more about machine learning basics? Don’t forget Google has a free online crash course to get you up to speed.
Neural networks are computer systems that are vaguely inspired by the construction of animal brains, and much like human brains, can be trained to obey the whims of the almighty domestic cat. [EdjeElectronics] has built just such a system, and his cat is better off for it.
The build uses a Raspberry Pi, fitted with the Pi Camera board, to image the area around the back door of the house. A Python script regularly captures images and passes them to a TensorFlow neural network for object recognition. The TensorFlow network returns object type and positions to the Python script. This information can be used to determine if there is a cat in the frame, and if it is inside or outside. If the cat remains in position for ten consecutive frames, a text message is sent via Twilio, indicating to the owner to let the cat in or out, as the case may be.
Thirty years ago, object classification was a pie-in-the-sky technology, but now you can run it on a $30 computer to figure out where your pets are. What a time we live in! A similar solution to this problem may be a cat door that unlocks via facial recognition. Video after the break.
[Thanks to Baldpower for the tip!]
Continue reading “Neural Network Knows When Cat Wants To Go Outside”
One of the most common uses of neural networks is the generation of new content, given certain constraints. A neural network is created, then trained on source content – ideally with as much reference material as possible. Then, the model is asked to generate original content in the same vein. This generally has mixed, but occasionally amusing, results. The team at [Made by AI] had a go at generating Christmas songs using this very technique.
The team decided that the easiest way to train their model would be to use note data from MIDI files. MIDI versions of Christmas songs are readily available and provide a broad base with which to train the model. For a neural network, the team chose to use a Long-short Term Memory (LSTM) architecture. This is a model which is contextually sensitive, which is important when dealing with structured formats like music or language.
The neural network generated five tunes which you can listen to on the Made by AI Soundcloud page. The team notes their time was limited, and we think that with some further work and more adherence to musical concepts such as structure and repetition, it might be possible to generate something a little more catchy.
There are other applications for AI in music, too – like these intelligent musical prostheses.
Neural networks are a key technology in the field of machine learning. A common technique is training them with sample data, and then asking them to create something new in the same vein. AI researcher [Janelle Shane] decided to task a neural network with a fun task – inventing new kinds of pie.
Using the char-rnn library, the network was initially trained on a sample of 2237 pie recipe titles, sourced from around the internet. Early iterations struggled to even spell “pie”, but as the network improved, so did the results. Where we can’t imagine how one would even make a “Sweesh Pie Ipple Pie”, later results, such as the “Impossible Maple Spinach Apple Pie” seem far more cromulent by comparison.
At this point, [Janelle] decided to mix things up, stirring in a further sample consisting of the names of various cookies and apples. The data were carefully sorted such that the network still prioritized pies, but this additional data gave the model a richer library to draw from. This led to such home-baked classics as Flangerson’s Blusty Tart and Chicken Pineapple Cream Pie.
On the surface, it’s a fun project with whimsical output, but fundamentally it highlights how much can be accomplished these days by standing on the shoulders of giants, so to speak. We’ve seen [Janelle]’s output before, too – naming tomatoes, no less.