Fooling a Real Car with Adversarial Traffic Signs

Understand

The attacks on the neural-network-based classifiers using adversarial images have gained a lot of attention recently.

• An adversary can purposely generate an image that is indistinguishable from a innocent image for a human being but is incorrectly classified by the neural networks.
• The adversarial images do not need to be tuned to a particular architecture of the classifier - an image that fools one network can fool another one with a certain success rate.The published works mostly concentrate on the use of modified image files for attacks against the classifiers trained on the model databases.
• Although there exists a general understanding that such attacks can be carried in the real world as well, the works considering the real-world attacks are scarce.