Threshold Implementations of all 3x3 and 4x4 S-boxes

Side-channel attacks have proven many hardware implementations of cryptographic algorithms to be vulnerable. A recently proposed masking method, based on secret sharing and multi-party computation methods, introduces a set of sufficient requirements for implementations to be provably resistant against first-order DPA with minimal assumptions on the hardware. The original paper doesn\u27t describe how to construct the Boolean functions that are to be used in the implementation. In this paper, we derive the functions for all invertible $3 \times 3$, $4 \times 4$ S-boxes and the $6 \times 4$ DES S-boxes. Our methods and observations can also be used to accelerate the search for sharings of larger (e.g. $8 \times 8$) S-boxes. Finally, we investigate the cost of such protection

An FPGA Architecture and CAD Flow Supporting Dynamically Controlled Power Gating

© 2015 IEEE.Leakage power is an important component of the total power consumption in field-programmable gate arrays (FPGAs) built using 90-nm and smaller technology nodes. Power gating was shown to be effective at reducing the leakage power. Previous techniques focus on turning OFF unused FPGA resources at configuration time; the benefit of this approach depends on resource utilization. In this paper, we present an FPGA architecture that enables dynamically controlled power gating, in which FPGA resources can be selectively powered down at run-time. This could lead to significant overall energy savings for applications having modules with long idle times. We also present a CAD flow that can be used to map applications to the proposed architecture. We study the area and power tradeoffs by varying the different FPGA architecture parameters and power gating granularity. The proposed CAD flow is used to map a set of benchmark circuits that have multiple power-gated modules to the proposed architecture. Power savings of up to 83% are achievable for these circuits. Finally, we study a control system of a robot that is used in endoscopy. Using the proposed architecture combined with clock gating results in up to 19% energy savings in this application

Towards Phytoplankton Parasite Detection Using Autoencoders

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections

Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks

A deep neural network is a non-spiking artificial neural network which uses multiple structured layers to extract features from the input. Spiking neural networks are another type of artificial neural network which closely mimic biology with time dependent pulses to transmit information. Whetstone is a training algorithm for spiking deep neural networks. It modifies the back propagation algorithm, typically used in deep learning, to train a spiking deep neural network, by converting the activation function found in deep neural networks into a threshold used by a spiking neural network. This work converts a spiking deep neural network trained from Whetstone to a traditional spiking neural network in the TENNLab framework. This conversion decomposes the dot product operation found in the convolutional layer of spiking deep neural networks to synapse connections between neurons in traditional spiking neural networks. The conversion also redesigns the neuron and synapse structure in the convolutional layer to trade time for space. A new architecture is created in the TENNLab framework using traditional spiking neural networks, which behave the same as the spiking deep neural network trained by Whetstone before conversion. This new architecture verifies the converted spiking neural network behaves the same as the original spiking deep neural network. This work can convert networks to run on other architectures from TENNLab, and this allows networks from those architectures to be trained with back propagation from Whetstone. This expands the variety of training techniques available to the TENNLab architectures

Decomposed S-Boxes and DPA Attacks: A Quantitative Case Study using PRINCE

Lightweight ciphers become indispensable and inevitable in the ubiquitous smart devices. However, the security of ciphers is often subverted by various types of attacks, especially, implementation attacks such as side-channel attacks. These attacks emphasise the necessity of providing efficient countermeasures. In this paper, our contribution is threefold: First, we observe and resolve the inaccuracy in the well-known and widely used formula for estimation of the number of gate equivalents (GE) in shared implementation. Then we present the first quantitative study on the efficacy of Transparency Order (TO) of decomposed S-Boxes in thwarting a side-channel attack. Using PRINCE S-Box we observe that TO-based decomposed implementation has better DPA resistivity than the naive implementation. To benchmark the DPA resistivity of TO(decomposed S-Box) implementation we arrive at an efficient threshold implementation of PRINCE, which itself merits to be an interesting contribution

Recognition of Characters from Streaming Videos

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FINE-GRAINED OBJECT DETECTION

Object detection plays a vital role in many real-world computer vision applications such as selfdriving cars, human-less stores and general purpose robotic systems. Convolutional Neural Network(CNN) based Deep Learning has evolved to become the backbone of most computer vision algorithms, including object detection. Most of the research has focused on detecting objects that differ significantly e.g. a car, a person, and a bird. Achieving fine-grained object detection to detect different types within one class of objects from general object detection can be the next step. Fine-grained object detection is crucial to tasks like automated retail checkout. This research has developed deep learning models to detect 200 types of birds of similar size and shape. The models were trained and tested on CUB-200-2011 dataset. To the best of our knowledge, by attaining a mean Average Precision (mAP) of 71.5% we achieved an improvement of 5 percentage points over the previous best mAP of 66.2%

Object Tracking in Games using Convolutional Neural Networks

Computer vision research has been growing rapidly over the last decade. Recent advancements in the field have been widely used in staple products across various industries. The automotive and medical industries have even pushed cars and equipment into production that use computer vision. However, there seems to be a lack of computer vision research in the game industry. With the advent of e-sports, competitive and casual gaming have reached new heights with regard to players, viewers, and content creators. This has allowed for avenues of research that did not exist prior. In this thesis, we explore the practicality of object detection as applied in games. We designed a custom convolutional neural network detection model, SmashNet. The model was improved through classification weights generated from pre-training on the Caltech101 dataset with an accuracy of 62.29%. It was then trained on 2296 annotated frames from the competitive 2.5-dimensional fighting game Super Smash Brothers Melee to track coordinate locations of 4 specific characters in real-time. The detection model performs at a 68.25% accuracy across all 4 characters. In addition, as a demonstration of a practical application, we designed KirbyBot, a black-box adaptive bot which performs basic commands reactively based only on the tracked locations of two characters. It also collects very simple data on player habits. KirbyBot runs at a rate of 6-10 fps. Object detection has several practical applications with regard to games, ranging from better AI design, to collecting data on player habits or game characters for competitive purposes or improvement updates