MeshAdv: Adversarial Meshes for Visual Recognition

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters.Comment: Published in IEEE CVPR201

Damage Mechanisms in Tapered Composite Structures Under Static and Fatigue Loading

In this work an integrated computational/experimental approach was developed to validate the predictive capabilities of State-of-the-Art (SoA) Progressive Damage Analysis (PDA) methods and tools. Specifically, a tapered composite structure incorporating ply-drops typical in the aerospace industry to spatially vary structural thickness was tested under static tension and cyclic tension fatigue loads. The data acquired from these tests included quantitative metrics such as pre-peak stiffness, peak load, location of delamination damage onset, and growth of delaminations as functions of applied static and fatigue loads. It was shown that the PDA tools were able to predict the pre-peak stiffness and peak load within 10% of experimental average, thereby meeting and exceeding the pre-defined success criteria. Additionally, it was shown that the PDA tools were able to accurately predict the location of delamination onset and satisfactorily predict delamination growth under static tension loading. Overall, good correlations were achieved between modeling and experiments

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

The 2D Distribution of Iron Rich Ejecta in the Remnant of SN 1885 in M31

We present Hubble Space Telescope (HST) ultraviolet Fe I and Fe II images of the remnant of Supernova 1885 (S And) which is observed in absorption against the bulge of the Andromeda galaxy, M31. We compare these Fe I and Fe II absorption line images to previous HST absorption images of S And, of which the highest quality and theoretically cleanest is Ca II H & K. Because the remnant is still in free expansion, these images provide a 2D look at the distribution of iron synthesized in this probable Type Ia explosion, thus providing insights and constraints for theoretical SN Ia models. The Fe I images show extended absorption offset to the east from the remnant's center as defined by Ca II images and is likely an ionization effect due to self-shielding. More significant is the remnant's apparent Fe II distribution which consists of four streams or plumes of Fe-rich material seen in absorption that extend from remnant center out to about 10,000 km/s. This is in contrast to the remnant's Ca II absorption, which is concentrated in a clumpy, roughly spherical shell at 1000 to 5000 km/s but which extends out to 12,500 km/s. The observed distributions of Ca and Fe rich ejecta in the SN 1885 remnant are consistent with delayed detonation white dwarf models. The largely spherical symmetry of the Ca-rich layer argues against a highly anisotropic explosion as might result from a violent merger of two white dwarfs.Comment: 14 pages, 8 figures, and 1 table; revised to match ApJ published versio