Hongxiang Gu

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  Proof-of-Ownership

  We replace the energy and computation hungry proof-of-work measure with a novel and efficient proof-of-ownership method.

  Proof-of-work is a very important measure in blockchain technology. However, current proof-of-work implementations are very expensive and power hungry, wasting much energy and computational resources in vain. We present a proof-of-ownership method to be used in a private blockchain setup that utilize execution-simulation-gap of some dedicated hardware so that the blockchain is still protected by computation, but in a much more efficient way.

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  Protecting Neural Network Models using Neuron Watermark

  We present a method to watermark a trained neural network model. Our method utilizes ReLu dead neurons to implant a watermakr as a proof of ownership.

  As neural network models are being used in a wide range of applications, model sharing is becoming more needed than ever. However model sharing also put the intellectual property and the training effort in danger. In order to prove that a specific model is trainined by a specific individual using a specific design on a specific training dataset, we propose to use dead Relu neurons as a robust watermark in neural models. Our method is robust against network pruning, model compression, fine-tuning and parameter tuning.

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  Efficient and Secure Group Key Management in IoT using Multistage Interconnected PUF

  We propose a group key management scheme based on a novel PUF design: multistage interconnected physically unclonable function (MIPUF) to secure group communications in an energy-constrained environment.

  Secure group-oriented communication is crucial to a wide range of applications in Internet of Things (IoT). Security problems related to group-oriented communications in IoTbased applications placed in a privacy-sensitive environment have become a major concern along with the development of the technology. Unfortunately, many IoT devices are designed to be portable and light-weight; thus, their functionalities, including security modules, are heavily constrained by the limited energy resources (e.g., battery capacity). To address these problems, we propose a group key management scheme based on a novel PUF design: multistage interconnected physically unclonable function (MIPUF) to secure group communications in an energy-constrained environment. Our design is capable of performing key management tasks such as key distribution, key storage and rekeying securely and efficiently. We show that our design is secure against multiple attack methods and our experimental results show that our design saves 47.33% of global energy comparing to state-of-the-art Elliptic-curve cryptography (ECC)-based key management scheme on average.

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  Securing Interconnected PUF Network with Reconfigurability

  We present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs.

  Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. Recent advancement in technology has significantly compromised the security of PUFs. Machine learning-based attacks have been proven to be able to construct numerical models that predict various types of PUFs with high accuracy with a small set of challenge-response pairs (CRPs). To address the problem, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself provides high resilience against modeling attacks, the reconfiguration mechanism remaps the input-output mapping before an attacker could collect sucient CRPs. Experimental results show that all tested state-of-the-art machine learning attack methods have prediction accuracy of around 50% on a single bit output of a reconfigurable IPN.

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  Unsupervised Video Summarization using Generative Deep Neural Network

  We present a framework ReconstSum that utilizes LSTM-based autoencoder architecture to extract and select a sparse subset of video frames or keyshots that optimally represent the input video in an unsupervised manner.

  Video summaries come in many forms, from traditional single-image thumbnails, animated thumbnails, storyboards, to trailer-like video summaries. Content creators use the summaries to display the most attractive portion of their videos; the users use them to quickly evaluate if a video is worth watching. All forms of summaries are essential to video viewers, content creators, and advertisers. Often video content management systems have to generate multiple versions of summaries that vary in duration and presentational forms. We present a framework ReconstSum that utilizes LSTM-based autoencoder architecture to extract and select a sparse subset of video frames or keyshots that optimally represent the input video in an unsupervised manner. The encoder selects a subset from the input video while the decoder seeks to reconstruct the video from the selection. The goal is to minimize the difference between the original input video and the reconstructed video. Our method is easily extendable to generate a varietyof applications including static video thumbnails, animated thumbnails, storyboards and "trailer-like" highlights. We specifically study and evaluate two most popular use cases: thumbnail generation and storyboard generation. We demonstrate that our methods generate better results than the state-of-the-art techniques in both use cases.

Gallery

I enjoy traveling around the world exploring different cultures and histories. I have visited more than 20 countries and I am trying hard to extend my list of visited places every year. I love taking photos whereever I go, you can find some of photos below.