red highlighted objects/persons were missing in a dataset crucial for autonomous driving
Brad Dwyer found a lot of missing or omitted labels in a set that is used for training and testing autonomous dryving systems. »We did a hand-check of the 15,000 images in the widely used Udacity Dataset 2 and found problems with 4,986 (33%) of them.« Since this is a Open Source Dataset used primarily for educational purposes, but as the author found out obviously also for test cars on public streets, he published a corrected set at https://public.roboflow.ai/object-detection/self-driving-car
Besides the honorable work of Dwyer, these omissions lead to the larger question of the reliablity of many data sets which are being used for training.
Face-recognition respirator masks
Danielle Baskin created a website for computational mapping to convert facial features into an image printed onto the surface of N95 surgical masks without distortion. It is a reaction to the Corona virus epidemic and allows to unlock (aka trick) face id techniques of smart phones.
KodyKinzie: »Confirming critical facial recognition research by @tahkion regarding #juggalo makeup defeating detection and recognition using the #esp32 and #face_recognition/#openCV Python libraries. Results seem conclusive.«
Teenagers have come up with elaborated schemes to share instagram accounts and produce obfuscating data, in order to look at whatever they want to look at without being tracked individually.
»Each time she refreshed the Explore tab, it was a completely different topic, none of which she was interested in. That’s because Mosley wasn’t the only person using this account — it belonged to a group of her friends, at least five of whom could be on at any given time. Maybe they couldn’t hide their data footprints, but they could at least leave hundreds behind to confuse trackers.« Alfred Ng on Cnet.com
Paint Your Face Away is a drop-in digital face painting workshop by Shinji Toya. The development of the digital face painting tool for this session has been inspired by Frank Bowling’s paintings. Participants use the painter to create their profile pictures while running a real-time face detection on the image of a face being painted so that at one point the profile picture stops being detected by the computer vision through the painting process. In this way, the digital paint acts as a type of disruptive noise for the machine.
Artist Simon Weckert generates poison data by transporting 99 second hand smartphones in a handcart and generates a virtual traffic jam in Google Maps. Through this activity he shows that it is possible to turn a green street red. This in turn has an impact in the physical world by navigating cars on another route to avoid being stuck in traffic. Simon U Rock!
Oct 28, 2014 demonstration in Hongkong (CC Studio Incendo)
Umbrellas are practical when it comes to avoid automated face recognition from CCTV et cetera, since they are everyday items and can’t be effectively banned by authorities.
Protesters spray paint a surveillance camera in Hongkong in July 2019 using umbrellas
Generated Faces
Icons8 product designer Konstantin Zhabinskiy worked on a project of generating 100k faces (using GANs) from a total of 29.000 photographs that they photographed in-house. This has the advantage of consistent lightening and being able to photograph different angles of the same face.
For the time being they have open sourced a large data-set hoping for traktion. It can be used for avatar images and such – so if you ever wanted to pretend you look like a model, no wrinkles, perfect lightening, symmetric eyes and such, only a few GAN-glitches, go ahead and use them for your account.
“incognito” is an anti-recognition jewelry mask by design studio NOMA, Warsaw https://noma-studio.pl/en/incognito/ it reverses the nose-eye relation and that’s what we like about it. Once could definitly go out on street with this.
Covered
This creation by London based designer Richard Quinn gets you fully covered. It got some traktion, since Cardi B appeared at Paris Fashion Week in one of his body and face covers. Maybe a motorcycle helmet would be still obfuscating enough, but would you want to wear it on fashion week?
»Surveillance Detection Scout is a hardware and software stack that makes use of your Tesla’s cameras to tell you if you’re being followed in real-time. The name, as you likely gathered, pays homage to the ever-effective Surveillance Detection Route. When parked, Scout makes an excellent static surveillance practitioner as well, allowing you to run queries and establish patterns-of-life on detected persons.«
Researcher Truman Kain therefore uses Facenet Image Recognition training data and Plugs into Teslas public API. For License Plate Recognition he uses ALPR. To save the imagery created by the three Tesla front cameras, he uses a software called Tesla USB.
»Kain, a consultant for the security firm Tevora, also isn’t oblivious to his creation’s creep factor. He says the Surveillance Detection Scout demonstrates the kind of surveillance the data that self-driving cars already collect could enable.«
To adversarial.io this presents a use-case where you want to have adversarial patches on license plates (if that is not forbidden by law, because it presents some kind of obfuscation) and of course wear an adversarial t‑shirt of some kind… This case also reminds me of the speculation, that UBER at some point might make their cars more profitable, by using them as data collection drones.