arXiv subjects

Weiqi Luo

Publications and source records attributed to Weiqi Luo.

49 records · Page 3Linked to original sources

arXiv2021

Direct Fidelity Estimation of Quantum States using Machine Learning

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with $\pm1\%$ precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.

quant-ph↗

arXiv2021

Reinforcement Learning with Expert Trajectory For Quantitative Trading

In recent years, quantitative investment methods combined with artificial intelligence have attracted more and more attention from investors and researchers. Existing related methods based on the supervised learning are not very suitable for learning problems with long-term goals and delayed rewards in real futures trading. In this paper, therefore, we model the price prediction problem as a Markov decision process (MDP), and optimize it by reinforcement learning with expert trajectory. In the proposed method, we employ more than 100 short-term alpha factors instead of price, volume and several technical factors in used existing methods to describe the states of MDP. Furthermore, unlike DQN (deep Q-learning) and BC (behavior cloning) in related methods, we introduce expert experience in training stage, and consider both the expert-environment interaction and the agent-environment interaction to design the temporal difference error so that the agents are more adaptable for inevitable noise in financial data. Experimental results evaluated on share price index futures in China, including IF (CSI 300) and IC (CSI 500), show that the advantages of the proposed method compared with three typical technical analysis and two deep leaning based methods.

cs.LG↗

arXiv2020

DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks

Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset. Efforts have been made to hinder the attack but unfortunately, they may lead to a major overhead or impaired usability. In this paper, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense aginist membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another related dataset, and derives a model that underlyingly extracts the features from both datasets. Seeing that the model is obfuscated, membership inference fails, while the extracted features provide supports for usability. Extensive experiments have been conducted to validates our intuition. The model trained by DAMIA has a negligible footprint to the usability. Our experiment also excludes factors that may hinder the performance of DAMIA, providing a potential guideline to vendors and researchers to benefit from our solution in a timely manner.

cs.CR↗

arXiv2020

Peripheral-free Device Pairing by Randomly Switching Power

The popularity of Internet-of-Things (IoT) comes with security concerns. Attacks against wireless communication venues of IoT (e.g., Man-in-the-Middle attacks) have grown at an alarming rate over the past decade. Pairing, which allows the establishment of the secure communicating channels for IoT devices without a prior relationship, is thus a paramount capability. Existing secure pairing protocols require auxiliary equipment/peripheral (e.g., displays, speakers and sensors) to achieve authentication, which is unacceptable for low-priced devices such as smart lamps. This paper studies how to design a peripheral-free secure pairing protocol. Concretely, we design the protocol, termed SwitchPairing, via out-of-box power supplying chargers and on-board clocks, achieving security and economics at the same time. When a user wants to pair two or more devices, he/she connects the pairing devices to the same power source, and presses/releases the switch on/off button several times. Then, the press and release timing can be used to derive symmetric keys. We implement a prototype via two CC2640R2F development boards from Texas Instruments (TI) due to its prevalence. Extensive experiments and user studies are also conducted to benchmark our protocol in terms of efficiency and security.

cs.CR↗

arXiv2020

Universal Stego Post-processing for Enhancing Image Steganography

It is well known that the designing or improving embedding cost becomes a key issue for current steganographic methods. Unlike existing works, we propose a novel framework to enhance the steganography security via post-processing on the embedding units (i.e., pixel values and DCT coefficients) of stego directly. In this paper, we firstly analyze the characteristics of STCs (Syndrome-Trellis Codes), and then design the rule for post-processing to ensure the correct extraction of hidden message. Since the steganography artifacts are typically reflected on image residuals, we try to reduce the residual distance between cover and the modified stego in order to enhance steganography security. To this end, we model the post-processing as a non-linear integer programming, and implement it via heuristic search. In addition, we carefully determine several important issues in the proposed post-processing, such as the candidate embedding units to be modified, the direction and amplitude of post-modification, the adaptive filters for getting residuals, and the distance measure of residuals. Extensive experimental results evaluated on both hand-crafted steganalytic features and deep learning based ones demonstrate that the proposed method can effectively enhance the security of most modern steganographic methods both in spatial and JPEG domains.

cs.MM↗

arXiv2019

Onionchain: Towards Balancing Privacy and Traceability of Blockchain-Based Applications

With the popularity of Blockchain comes grave security-related concerns. Achieving privacy and traceability simultaneously remains an open question. Efforts have been made to address the issues, while they may subject to specific scenarios. This paper studies how to provide a more general solution for this open question. Concretely, we propose Onionchain, featuring a suite of protocols, offering both traceability and privacy. As the term implies, our Onionchain is inspired by Onion routing. We investigate the principles of Onion routing carefully and integrate its mechanism together with Blockchain technology. We advocate the Blockchain community to adopt Onionchain with the regards of privacy and traceability. To this end, a case-study of Onionchain, which runs in the context of Vehicular Ad Hoc Networks (VANETs), is proposed, providing the community a guideline to follow. Systematic security analysis and extensive experiments are also conducted to validate our secure and cost-effective Onionchain.

cs.CR↗

arXiv2019

Long Optimal or Small-Defect LRC Codes with Unbounded Minimum Distances

A code over a finite field is called locally recoverable code (LRC) if every coordinate symbol can be determined by a small number (at most r, this parameter is called locality) of other coordinate symbols. For a linear code with length n, dimension k and locality r, its minimum distance d satisfies a Singleton-like bound. A code attaining this bound is called optimal. Many families of optimal locally recoverable codes have been constructed by using different techniques in finite fields or algebraic curves. However no optimal LRC code over a general finite field of q elements with the length n around the square of q, the locality r larger than or equal to 24 and the minimum distance d larger than or equal to 9 has been constructed. In this paper for any given finite field of q elements, any given r between 1 and q-1 and given d in certain range, we give an optimal LRC code with length n around the square of q, locality r and minimum distance d. This is the only known family of optimal LRC codes with lengths n around the square of q and unbounded localities and minimum distances d larger than or equal to 9. We also gives an asymptotic bound for q-ary r locality LRC codes better than the previously konwn bound. Many long r-locality LRC codes with small defects are also constructed.

cs.IT↗

arXiv2019

Enhancing JPEG Steganography using Iterative Adversarial Examples

Convolutional Neural Networks (CNN) based methods have significantly improved the performance of image steganalysis compared with conventional ones based on hand-crafted features. However, many existing literatures on computer vision have pointed out that those effective CNN-based methods can be easily fooled by adversarial examples. In this paper, we propose a novel steganography framework based on adversarial example in an iterative manner. The proposed framework first starts from an existing embedding cost, such as J-UNIWARD in this work, and then updates the cost iteratively based on adversarial examples derived from a series of steganalytic networks until achieving satisfactory results. We carefully analyze two important factors that would affect the security performance of the proposed framework, i.e. the percentage of selected gradients with larger amplitude and the adversarial intensity to modify embedding cost. The experimental results evaluated on three modern steganalytic models, including GFR, SCA-GFR and SRNet, show that the proposed framework is very promising to enhance the security performances of JPEG steganography.

cs.MM↗

arXiv2019

A Hybrid and Universal Blind Quantum Computation

In blind quantum computation (BQC), a client delegates her quantum computation to a server with universal quantum computers who learns nothing about the client's private information. In measurement-based BQC model, entangled states are generally used to realize quantum computing. However, to generate a large-scale entangled state in experiment becomes a challenge issue. In circuit-based BQC model, single-qubit gates can be realized precisely, but entangled gates are probabilistically successful. This remains a challenge to realize entangled gates with a deterministic method in some systems. To solve above two problems, we propose the first hybrid universal BQC protocol based on measurements and circuits, where the client prepares single-qubit states and the server performs universal quantum computing. We analyze and prove the correctness, blindness and verifiability of the proposed protocol.

quant-ph↗

arXiv2019

Local distinguishability of quantum states in bipartite systems

In this article, we show a sufficient and necessary condition for locally distinguishable bipartite states via one-way local operations and classical communication (LOCC). With this condition, we present some minimal structures of one-way LOCC indistinguishable quantum state sets. As long as an indistinguishable subset exists in a state set, the set is not distinguishable. We also list several distinguishable sets as instances.

quant-ph↗

arXiv2017

Image Processing Operations Identification via Convolutional Neural Network

In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.

cs.MM↗

arXiv2017

Anti-Forensics of Camera Identification and the Triangle Test by Improved Fingerprint-Copy Attack

The fingerprint-copy attack aims to confuse camera identification based on sensor pattern noise. However, the triangle test shows that the forged images undergone fingerprint-copy attack would share a non-PRNU (Photo-response nonuniformity) component with every stolen image, and thus can detect fingerprint-copy attack. In this paper, we propose an improved fingerprint-copy attack scheme. Our main idea is to superimpose the estimated fingerprint into the target image dispersedly, via employing a block-wise method and using the stolen images randomly and partly. We also develop a practical method to determine the strength of the superimposed fingerprint based on objective image quality. In such a way, the impact of non-PRNU component on the triangle test is reduced, and our improved fingerprint-copy attack is difficultly detected. The experiments evaluated on 2,900 images from 4 cameras show that our scheme can effectively fool camera identification, and significantly degrade the performance of the triangle test simultaneously.

cs.MM↗

arXiv2015

Identification of Image Operations Based on Steganalytic Features

Image forensics have attracted wide attention during the past decade. Though many forensic methods have been proposed to identify image forgeries, most of them are targeted ones, since their proposed features are highly dependent on the image operation under investigation. The performance of the well-designed features for detecting the targeted operation usually degrades significantly for other operations. On the other hand, a wise attacker can perform anti-forensics to fool the existing forensic methods, making countering anti-forensics become an urgent need. In this paper, we try to find a universal feature to detect various image processing and anti-forensic operations. Based on our extensive experiments and analysis, we find that any image processing/anti-forensic operations would inevitably modify many image pixels. This would change some inherent statistics within original images, which is similar to the case of steganography. Therefore, we model image processing/anti-forensic operations as steganography problems, and propose a detection strategy by applying steganalytic features. With some advanced steganalytic features, we are able to detect various image operations and further identify their types. In our experiments, we have tested several steganalytic features on 11 different kinds of typical image processing operations and 4 kinds of anti-forensic operations. The experimental results have shown that the proposed strategy significantly outperforms the existing forensic methods in both effectiveness and universality.

cs.MM↗