Camouflaged Graffiti on Road Signs Can Fool Machine Learning Models - The New Stack

To carry out their experiments, the team trained their model in TensorFlow, employing a public dataset of road signs. While the dataset of a few thousand training examples was relatively small, the results plainly show the potential vulnerabilities of deep learning artificial neural networks used in autonomous driving systems when real objects are modified. "Unlike prior work, […] here we focus on evasion attacks where attackers can only modify the testing data instead of training data (poisoning attack)," explained the researchers. "In evasion attacks, an attacker can only change existing physical road signs. Here we assume that an attacker gains access to the classifier after it has been trained ('white-box' access)."