The thermal camera can capture keyboard surface temperature change after a human's touch. This phenomenon may be used to steal users' passwords physically. In this paper, based on the study of thermal dynamics of keyboards, we design a password break system using an infrared thermal camera. First, we build a signal model to describe the dynamic process of temperature change on the keyboard using Newton's law of cooling. Next, we develop a maximum likelihood parameter estimation algorithm to estimate the keystroke time instants. Then, by maximizing the probability of key order arrangement, a novel password breaking algorithm is developed. Our algorithm is tested using simulated data as well as real-world data. Experiment results show that our algorithm is effective for physical password breaking using thermal characteristics. Based on our results, we discuss strategies for password protection at the end.
Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer's stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes, and different audio recorder positions.
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Neural networks are a set of biologically inspired algorithms that can be used to recognize patterns. Deep neural networks (DNNs) are neural networks that have much more layers in depth than traditional neural networks. DNNs are thus capable of learning high-level features with more complexity and abstraction than shallower neural networks. The use of DNNs has seen explosive growth in the past few years. Currently, DNNs are widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. The article Efficient Processing of Deep Neural Networks: A Tutorial and Survey by V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer published in Proceedings of the IEEE in December, 2017, provides a comprehensive tutorial and survey coverage of the recent advances toward enabling efficient processing of DNNs.
The authors provide an overview of DNNs before discussing various hardware platforms and architectures that support DNNs, and highlighting key trends in reducing the computation cost of DNNs through various techniques such as hardware design changes or via joint hardware design and DNN algorithm changes. The article also summarizes various development resources that will enable the reader to quickly get started in this field, and highlights important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. The article concludes with recent implementation trends and opportunities.
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