Previous work has shown that Deep Neural Networks (DNNs), including those currently in use in many fields, are extremely vulnerable to maliciously crafted inputs, known as adversarial examples. Despite extensive and thorough research of adversarial examples in many areas, adversarial 3D data, such as point clouds, remain comparatively unexplored. The study of adversarial 3D data is crucial considering its impact in real-life, high-stakes scenarios including autonomous driving. In this paper, we propose a novel adversarial attack against PointNet++, a deep neural network that performs classification and segmentation tasks using features learned directly from raw 3D points. In comparison to existing works, our attack generates not only adversarial point clouds, but also robust adversarial objects that in turn generate adversarial point clouds when sampled both in simulation and after construction in real world. We also demonstrate that our objects can bypass existing defense mechanisms designed especially against adversarial 3D data.

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We report our experiences in designing and implementing several hardware Trojans within the framework of the Embedded System Challenge competition that was held as part of the Cyber Security Awareness Week (CSAW) at the Polytechnic Institute of New York University in October 2008. Due to the globalization of the Integrated Circuit (IC) manufacturing industry, hardware Trojans constitute an increasingly probable threat to both commercial and military applications. With traditional testing methods falling short in the quest of finding hardware Trojans, several specialized detection methods have surfaced. To facilitate research in this area, a better understanding of what Hardware Trojans would look like and what impact they would incur to an IC is required. To this end, we present eight distinct attack techniques employing Register Transfer Level (RTL) hardware Trojans to compromise the security of an Alpha encryption module implemented on a Digilent BASYS Spartan-3 FPGA board. Our work, which earned second place in the aforementioned competition, demonstrates that current RTL designs are, indeed, quite vulnerable to hardware Trojan attacks.

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