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Operator coherence dynamics in Grover's quantum search algorithm

Physical Review A 2019年第1期100卷 012349-012349页

作者： Minghua Pan Daowen Qiu Institute of Computer Science Theory School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou 510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University 510006 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China Instituto de Telecomunicações Departamento de Matemática Instituto Superior Técnico Av. Rovisco Pais 1049-001 Lisbon Portugal

Coherence is one of the most basic concepts and resources in quantum information. To clear the role coherence plays on the essential operator level in Grover's search algorithm, here we discuss the coherence dynamics of the state after each basic operator is applyied. As it is known, Grover's search algorithm repeats the application of Grover operator G, which can be decomposed into G=H⊗nPH⊗nO, where H is Hadamard operator, P is the condition phase-shift operator, and O is the oracle operator. First, we show that O and P are incoherent operators while H⊗n is coherent. Second, we prove that the amount of the operator coherence of the first H⊗n and the operator coherence produced or depleted by H⊗n depends not only on the size of the database and the success probability, but also on target states. Moreover, the amount of operator coherence is larger when the superposition state of targets is entangled rather than product. Third, we show that the two H⊗n have different effects on coherence that one produces coherence and the other depletes coherence, and the depletion plays a major role. Therefore, the coherence is vibrating during the search process and the overall effect is that coherence is in depletion.

关键词： Quantum algorithms Quantum correlations in quantum information

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Universal Physical Camouflage Attacks on Object Detectors

Universal Physical Camouflage Attacks on Object Detectors

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lifeng Huang Chengying Gao Yuyin Zhou Cihang Xie Alan L. Yuille Changqing Zou Ning Liu School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Department of Computer Science The Johns Hopkins University Max Planck Institute for Informatics University of Maryland College Park

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we study physical adversarial attacks on object detectors in the wild. Previous works mostly craft instance-dependent perturbations only for rigid or planar objects. To this end, we propose to learn an adversarial pattern to effectively attack all instances belonging to the same object category, referred to as Universal Physical Camouflage Attack (UPC). Concretely, UPC crafts camouflage by jointly fooling the region proposal network, as well as misleading the classifier and the regressor to output errors. In order to make UPC effective for non-rigid or non-planar objects, we introduce a set of transformations for mimicking deformable properties. We additionally impose optimization constraint to make generated patterns look natural to human observers. To fairly evaluate the effectiveness of different physical-world attacks, we present the first standardized virtual database, AttackScenes, which simulates the real 3D world in a controllab.e and reproducible environment. Extensive experiments suggest the superiority of our proposed UPC compared with existing physical adversarial attackers not only in virtual environments (AttackScenes), but also in real-world physical environments.

关键词： Proposals Detectors Semantics Perturbation methods Strain Optimization Three-dimensional displays

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Improving Fast Segmentation With Teacher-student Learning Oct 20

arXiv

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arXiv 2018年

作者： Xie, Jiafeng Shuai, Bing Hu, Jian-Fang Lin, Jingyang Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Nanyang Technological University Singapore Guangdong Key Laboratory of Information Security Technology Key Laboratory of Machine Intelligence and Advanced Computing MOE

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks. However, these models are very heavy and generally suffer from low inference speed, which limits their application scenarios in practice. Meanwhile, existing fast segmentation models usually fail to obtain satisfactory segmentation accuracies on public benchmarks. In this paper, we propose a teacher-student learning framework that transfers the knowledge gained by a heavy and better performed segmentation network (i.e. teacher) to guide the learning of fast segmentation networks (i.e. student). Specifically, both zero-order and first-order knowledge depicted in the fine annotated images and unlab.led auxiliary data are transferred to regularize our student learning. The proposed method can improve existing fast segmentation models without incurring extra computational overhead, so it can still process images with the same fast speed. Extensive experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate that the proposed teacher-student learning framework is able to significantly boost the performance of student network. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

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Inserting anybody in diffusion models via celeb basis 23

Inserting anybody in diffusion models via celeb basis

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Ge Yuan Xiaodong Cun Yong Zhang Maomao Li Chenyang Qi Xintao Wang Ying Shan Huicheng Zheng School of Computer Science and Engineering Sun Yat-sen University and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China and Guangdong Key Laboratory of Information Security Technology and Tencent AI Lab Tencent AI Lab The Hong Kong University of Science and Technology School of Computer Science and Engineering Sun Yat-sen University and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China and Guangdong Key Laboratory of Information Security Technology

Exquisite demand exists for customizing the pretrained large text-to-image model, e.g. Stable Diffusion, to generate innovative concepts, such as the users themselves. However, the newly-added concept from previous customization methods often shows weaker combination abilities than the original ones even given several images during training. We thus propose a new personalization method that allows for the seamless integration of a unique individual into the pre-trained diffusion model using just one facial photograph and only 1024 learnable parameters under 3 minutes. So we can effortlessly generate stunning images of this person in any pose or position, interacting with anyone and doing anything imaginable from text prompts. To achieve this, we first analyze and build a well-defined celeb basis from the embedding space of the pre-trained large text encoder. Then, given one facial photo as the target identity, we generate its own embedding by optimizing the weight of this basis and locking all other parameters. Empowered by the proposed celeb basis, the new identity in our customized model showcases a better concept combination ability than previous personalization methods. Besides, our model can also learn several new identities at once and interact with each other where the previous customization model fails to. Project page is at: http://***. Code is at: https://***/ygtxr1997/CelebBasis.

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Transmission of Gaussian Source Using Ternary Convolutional Low Density Generator Matrix Codes

Transmission of Gaussian Source Using Ternary Convolutional ...

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International Conference on Communication Systems

作者： Qiaoqiao Wu Meiying Ji Jiachen Sun Xiao Ma School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781538678657;9781538678640

In this paper, we propose a new joint source-channel coding (JSCC) scheme to transmit a Gaussian source over an additive white Gaussian noise (AWGN) channel, where the channel input constraint is on energy per source symbol rather than per channel symbol. The JSCC scheme is based on an embedded Lloyd-Max quantizer with adjustable quantization levels and the ternary convolutional low density generator matrix (LDGM) codes, which are easily configurable in the sense that any rational code rate can be achieved without complicated optimization. In our scheme, first, a Gaussian source is quantized into ternary symbols, which can be formatted into different symbol planes according to their contribution to the distortion. Second, the symbol planes are encoded by a ternary convolutional LDGM code, where the relatively important symbol plane is encoded with a lower code rate. Third, the coded sequence is modulated in 3-PAM and then transmitted over the channel. Numerical results show that the proposed scheme performs close to the Shannon limit, and that the distortion can be lowered down by increasing the number of quantization levels.

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The minority matters: a diversity-promoting collab.rative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collab.rative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collab.rative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collab.rative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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PGD-Imp: Rethinking and Unleashing Potential of Classic PGD with Dual Strategies for Imperceptible Adversarial Attacks

arXiv

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arXiv 2024年

作者： Li, Jin Yu, Zitong He, Ziqiang Wang, Z. Jane Kang, Xiangui Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-Sen University China School of Computing and Information Technology Great Bay University China Electrical and Computer Engineering Dept University of British Columbia Canada

Imperceptible adversarial attacks have recently attracted increasing research interests. Existing methods typically incorporate external modules or loss terms other than a simple lp-norm into the attack process to achieve imperceptibility, while we argue that such additional designs may not be necessary. In this paper, we rethink the essence of imperceptible attacks and propose two simple yet effective strategies to unleash the potential of PGD, the common and classical attack, for imperceptibility from an optimization perspective. Specifically, the Dynamic Step Size is introduced to find the optimal solution with minimal attack cost towards the decision boundary of the attacked model, and the Adaptive Early Stop strategy is adopted to reduce the redundant strength of adversarial perturbations to the minimum level. The proposed PGD-Imperceptible (PGD-Imp) attack achieves state-of-the-art results in imperceptible adversarial attacks for both untargeted and targeted scenarios. When performing untargeted attacks against ResNet-50, PGD-Imp attains 100% (+0.3%) ASR, 0.89 (-1.76) l2 distance, and 52.93 (+9.2) PSNR with 57s (-371s) running time, significantly outperforming existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. China Cluster and Grid Computing Lab. China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

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KA$^{2}$2SE: key-Aggregation Authorized Searchable Encryption Scheme for data Sharing in Wireless Sensor Networks

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IEEE Transactions on Services Computing 2024年第1期18卷 226-238页

作者： Haijiang Wang Jianting Ning Wei Wu Chao Lin Kai Zhang School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China School of Cyber Science and Engineering Wuhan University Wuhan China Faculty of Data Science City University of Macau Macau China College of Education Sciences Hong Kong University of Science and Technology Guangzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China Guangdong Key Laboratory of Blockchain Security Guangzhou University Guangzhou China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China

As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “ key Forge Attack ” and “ Trapdoor Forge Attack ” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA $^security$ SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA $^security$ SE and prove its security. Finally, empirical evaluations demonstrate that KA $^security$ SE is computationally efficient in comparison with existing schemes.

关键词： Encryption Cryptography keyword search Wireless sensor networks Servers Privacy Cloud computing security Probabilistic logic Authorization

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