BigSnarf blog

Infosec FTW

Emergency Vehicle Alerting

google-car-1

Emergency Vehicle Alerting system that warns drivers when they are approaching an ambulance, fire engine, police or rescue squad using emergency lights.
A warning system for alerting the driver of a private vehicle that an emergency vehicle is approaching is disclosed. The system includes a receiver and a display panel mounted in the private vehicle, and at least two infrared receivers mounted on the private vehicle. The display panel mounted in the private vehicle including indicating devices that allow the driver of the private vehicle to know of the approaching emergency vehicle as well as the direction to move in order to yield the right of way to an approaching emergency vehicle.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.399.7544&rep=rep1&type=pdf

http://patents.justia.com/patent/9278689

https://www.google.com/patents/US7057528

https://www.google.com/patents/US20110187559

http://www.google.ca/patents/US7061402

http://www.ifv.nl/kennisplein/Documents/20082-detection-of-emergency-vehicles.pdf

 

Car accident detection – sounds of crashing

hydroplaning noise https://arxiv.org/abs/1511.07035

http://karol.piczak.com/papers/Piczak2015-ESC-ConvNet.pdf

Detecting car part failures with sounds and deep learning http://www.vocativ.com/387520/artificial-intelligence-car/

http://www.cs.tut.fi/sgn/arg/music/tuomasv/parascandolo-icassp2016.pdf

PID Control – SDC Neural Network Cruise Control

CNN – Image RotationInvariance

screen-shot-2017-01-26-at-11-00-41-pm

 

Harmonic Networks: Deep Translation and Rotation Equivariance

 https://arxiv.org/abs/1612.04642v1

Deep Learning is awesome and stupid

road-runner-tunnel-car

We need to look beyond the hype cycle on Deep Learning. Deep Learning is ripe for new discoveries by security researchers.

Deep Learning very popular in recent years. Everyone is talking about Deep Learning and reference AI playing games and beating world champions in Go.

When you call out to Siri or Google for answers, Deep Learning is the technology solving those hard problems on the backend. Deep Learning has taken over image recognition and speech recognition. Self driving cars depend on Deep Learning.

Where do you start? What security controls would you put in place? How do you even secure Deep Learning?

Code and papers below on poisoning the system and creating examples to evade some basic vision systems.

These systems are weak. Securing Deep Learning systems are a green field.

 

17-02-enigma

 

Optical Illusions

http://www.michaelbach.de/ot/

Can’t wait for hackers. We need to look beyond the hype cycle on Deep Learning. Deep Learning is ripe for new discoveries by security researchers.

https://github.com/openai/cleverhans

Aston Martin will focus on cybersecurity before developing a self-driving Lagonda

http://karpathy.github.io/2015/03/30/breaking-convnets/

http://cs.stanford.edu/people/karpathy/break_linear_classifier.ipynb

https://people.eecs.berkeley.edu/~tygar/papers/SML/EECS-2008-43.pdf

https://da-data.blogspot.ca/2017/01/finding-bugs-in-tensorflow-with.html

https://people.eecs.berkeley.edu/~tygar/papers/SML2/Adversarial_AISEC.pdf

https://arxiv.org/pdf/1606.06565.pdf

https://arxiv.org/pdf/1412.6572v3.pdf

https://arxiv.org/pdf/1701.04079v1.pdf

https://arxiv.org/abs/1610.05820

http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf

https://arxiv.org/abs/1312.6199

https://arxiv.org/pdf/1412.1897v4.pdf

https://arxiv.org/abs/1609.02943

https://arxiv.org/abs/1610.05820

https://arxiv.org/pdf/1611.01236.pdf

https://people.eecs.berkeley.edu/~tygar/papers/SML/EECS-2008-43.pdf

https://arxiv.org/abs/1611.03814

https://arxiv.org/abs/1611.03814

https://www.endgame.com/blog/endgame-research-aisec-deep-dga

https://conf.startup.ml/blog/adversarial

http://www.slideshare.net/pragroup/secure-kernel-machines-against-evasion-attacks

https://da-data.blogspot.ca/2017/01/finding-bugs-in-tensorflow-with.html

https://arxiv.org/pdf/1606.06565.pdf

https://arxiv.org/pdf/1312.6199v1.pdf

https://arxiv.org/pdf/1701.04079v1.pdf

http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf

https://arxiv.org/abs/1312.6199

https://arxiv.org/pdf/1412.1897v4.pdf

http://cacm.acm.org/magazines/2016/11/209133-learning-securely/fulltext

https://arxiv.org/abs/1609.02943

https://arxiv.org/pdf/1412.6572.pdf

https://arxiv.org/abs/1611.03814

https://arxiv.org/abs/1611.03814

https://people.eecs.berkeley.edu/~tygar/papers/SML2/Adversarial_AISEC.pdf

http://composition.al/blog/2016/09/29/thoughts-on-adversarial-examples-in-the-physical-world/

https://conf.startup.ml/blog/adversarial

Main

https://people.eecs.berkeley.edu/~tygar/papers/SML2/Adversarial_AISEC.pdf

https://mascherari.press/introduction-to-adversarial-machine-learning/

https://arxiv.org/pdf/1606.04435v1.pdf

https://www.wired.com/2017/02/hacked-android-phones-unlock-millions-cars/

https://www.ecnmag.com/news/2017/02/cybersecurity-risk-self-driving-cars#.WKclP-zn0xU.twitter

 

 

Agent based self driving car simulator

Backprop

DeepSounds

Style transfer – NIR –> RGB

Single Shot Detector for pedestrians in Autoware

Mathz 4 ML