Biblio

Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access to the model parameters, such as gradient information, to alter the input or by attacking a substitute model and transferring those malicious examples over to attack the victim model. Specifically, one of these attack algorithms, Robust Physical Perturbations (RP2), generates adversarial images of stop signs with black and white stickers to achieve high targeted misclassification rates against standard-architecture traffic sign classifiers. In this paper, we propose BlurNet, a defense against the RP2 attack. First, we motivate the defense with a frequency analysis of the first layer feature maps of the network on the LISA dataset, which shows that high frequency noise is introduced into the input image by the RP2 algorithm. To remove the high frequency noise, we introduce a depthwise convolution layer of standard blur kernels after the first layer. We perform a blackbox transfer attack to show that low-pass filtering the feature maps is more beneficial than filtering the input. We then present various regularization schemes to incorporate this lowpass filtering behavior into the training regime of the network and perform white-box attacks. We conclude with an adaptive attack evaluation to show that the success rate of the attack drops from 90% to 20% with total variation regularization, one of the proposed defenses.

The hardware and low-level software in many mobile devices are capable of mobile-to-mobile communication, including ad-hoc 802.11, Bluetooth, and cognitive radios. We have started to leverage this capability to provide interpersonal communication both over infrastructure networks (the Internet), and over ad-hoc and delay-tolerant networks composed of the mobile devices themselves. This network is decentralized in the sense that it can function without any infrastructure, but does take advantage of infrastructure connections when available. All interpersonal communication is encrypted and authenticated so packets may be carried by devices belonging to untrusted others. The decentralized model of security builds a flexible trust network on top of the social network of communicating individuals. This social network can be used to prioritize packets to or from individuals closely related by the social network. Other packets are prioritized to favor packets likely to consume fewer network resources. Each device also has a policy that determines how many packets may be forwarded, with the goal of providing useful interpersonal communications using at most 1% of any given resource on mobile devices. One challenge in a fully decentralized network is routing. Our design uses Rendezvous Points (RPs) and Distributed Hash Tables (DHTs) for delivery over infrastructure networks, and hop-limited broadcast and Delay Tolerant Networking (DTN) within the wireless ad-hoc network.