Lockdown: Dynamic Control-Flow Integrity

Applications written in low-level languages without type or memory safety are especially prone to memory corruption. Attackers gain code execution capabilities through such applications despite all currently deployed defenses by exploiting memory corruption vulnerabilities. Control-Flow Integrity (CFI) is a promising defense mechanism that restricts open control-flow transfers to a static set of well-known locations. We present Lockdown, an approach to dynamic CFI that protects legacy, binary-only executables and libraries. Lockdown adaptively learns the control-flow graph of a running process using information from a trusted dynamic loader. The sandbox component of Lockdown restricts interactions between different shared objects to imported and exported functions by enforcing fine-grained CFI checks. Our prototype implementation shows that dynamic CFI results in low performance overhead.Comment: ETH Technical Repor

Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with high accuracy while using few coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds interactively. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, also for high-quality production of animated content.Comment: ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2017

The LHC Discovery Potential of a Leptophilic Higgs

In this work, we examine a two-Higgs-doublet extension of the Standard Model in which one Higgs doublet is responsible for giving mass to both up- and down-type quarks, while a separate doublet is responsible for giving mass to leptons. We examine both the theoretical and experimental constraints on the model and show that large regions of parameter space are allowed by these constraints in which the effective couplings between the lightest neutral Higgs scalar and the Standard-Model leptons are substantially enhanced. We investigate the collider phenomenology of such a "leptophilic" two-Higgs-doublet model and show that in cases where the low-energy spectrum contains only one light, CP-even scalar, a variety of collider processes essentially irrelevant for the discovery of a Standard Model Higgs boson (specifically those in which the Higgs boson decays directly into a charged-lepton pair) can contribute significantly to the discovery potential of a light-to-intermediate-mass (m_h < 140 GeV) Higgs boson at the LHC.Comment: 25 pages, LaVTeX, 11 figures, 1 tabl