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A differential public PUF design for lightweight authentication 16

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A differential public PUF design for lightweight authenticat...

2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

Overview Paper A Comprehensive Survey of Digital Image Steganography and Steganalysis

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APSIPA Transactions on Signal and Information Processing 2024年第1期13卷

作者： Luo, Weiqi Wei, Kangkang Li, Qiushi Ye, Miaoxin Tan, Shunquan Tang, Weixuan Huang, Jiwu School of Computer Science and Engineering Sun Yat-sen University China School of Software Nanchang University China Shenzhen Key Laboratory of Media Security Shenzhen University China Institute of Artificial Intelligence Guangzhou University China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University China

In the realm of digital communications, steganography and steganalysis have become a solution for securely exchanging covert information. This survey initiates with an exploration of the widely used passive-warden scenario model, analyzing its significance, key performance indicators, relevant databases, and clarifying some commonly misunderstood fundamental concepts associated with this model. Subsequently, the paper comprehensively examines the evolution and current state of digital image steganography and steganalysis, highlighting the transition from traditional handcrafted based methods to sophisticated deep learning based techniques developed over the past two decades. It offers thorough descriptions and evaluations of typical methods in both steganography and steganalysis, with a particular emphasis on deep learning-based techniques that have emerged in recent years. Furthermore, the survey identifies significant challenges currently faced in translating theoretical research into practical applications. By integrating these insights, the survey not only charts the historical development and technological advancements in steganography and steganalysis but also establishes a proactive agenda for future research aimed at enhancing security in covert communications. © 2024 W. Luo, K. Wei, Q. Li, M. Ye, S. Tan, W. Tang and J. Huang.

关键词： Steganalysis steganography

Thqa: A Perceptual Quality Assessment Database for Talking Heads

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Thqa: A Perceptual Quality Assessment Database for Talking H...

IEEE International Conference on Image Processing

作者： Yingjie Zhou Zicheng Zhang Wei Sun Xiaohong Liu Xiongkuo Min Zhihua Wang Xiao-Ping Zhang Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China PengCheng Laboratory China Guangdong Lab of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Tsinghua Shenzhen International Graduate School Tsinghua University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In the realm of media technology, digital humans have gained prominence due to rapid advancements in computer technology. However, the manual modeling and control required for the majority of digital humans pose significant obstacles to efficient development. The speech-driven methods offer a novel avenue for manipulating the mouth shape and expressions of digital humans. Despite the proliferation of driving methods, the quality of many generated talking head (TH) videos remains a concern, impacting user visual experiences. To tackle this issue, this paper introduces the Talking Head Quality Assessment (THQA) database, featuring 800 TH videos generated through 8 diverse speechdriven methods. Extensive experiments affirm the THQA database’s richness in character and speech features. Subsequent subjective quality assessment experiments analyze correlations between scoring results and speech-driven methods, ages, and genders. In addition, experimental results show that mainstream image and video quality assessment methods have limitations for the THQA database, underscoring the imperative for further research to enhance TH video quality assessment. The THQA database is publicly accessible at https://***/zyj-2000/THQA.

关键词： Visualization Databases Shape Image processing Mouth Manuals Media

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading

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IEEE Journal of Biomedical and Health Informatics 2024年第5期29卷 3184-3193页

作者： Xiaoling Luo Qihao Xu Zhihua Wang Chao Huang Chengliang Liu Xiaopeng Jin Jianguo Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Shenzhen China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with diabetes. In these years, various deep-learning approaches for DR grading have been proposed. Most previous DR grading models are trained using the dataset of single-field fundus images, but the entire retina cannot be fully visualized in a single field of view. There are also problems of scattered location and great differences in the appearance of lesions in fundus images. To address the limitations caused by incomplete fundus features, and the difficulty in obtaining lesion information. This work introduces a novel multi-view DR grading framework, which solves the problem of incomplete fundus features by jointly learning fundus images from multiple fields of view. Furthermore, the proposed model combines multi-view inputs such as fundus images and lesion snapshots. It utilizes heterogeneous convolution blocks (HCB) and scalable self-attention classes (SSAC), which enhance the ability of the model to obtain lesion information. The experimental results show that our proposed method performs better than the benchmark methods on the large-scale dataset.

关键词： Lesions Retina Diabetic retinopathy Convolutional neural networks Feature extraction Diseases Bioinformatics

LFMamba: Light Field Image Super-Resolution with State Space Model

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arXiv 2024年

作者： Xia, Wang Lu, Yao Wang, Shunzhou Wang, Ziqi Xia, Peiqi Zhou, Tianfei School of Computer Beijing Institute of Technology Beijing100081 China Guangdong Laboratory of Machine Perception and Intelligent Computing Department of Engineering Shenzhen MSU-BIT University Shenzhen518172 China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen518055 China

Recent years have witnessed significant advancements in light field image super-resolution (LFSR) owing to the progress of modern neural networks. However, these methods often face challenges in capturing long-range dependencies (CNN-based) or encounter quadratic computational complexities (Transformer-based), which limit their performance. Recently, the State Space Model (SSM) with selective scanning mechanism (S6), exemplified by Mamba, has emerged as a superior alternative in various vision tasks compared to traditional CNN- and Transformer-based approaches, benefiting from its effective long-range sequence modeling capability and linear-time complexity. Therefore, integrating S6 into LFSR becomes compelling, especially considering the vast data volume of 4D light fields. However, the primary challenge lies in designing an appropriate scanning method for 4D light fields that effectively models light field features. To tackle this, we employ SSMs on the informative 2D slices of 4D LFs to fully explore spatial contextual information, complementary angular information, and structure information. To achieve this, we carefully devise a basic SSM block characterized by an efficient SS2D mechanism that facilitates more effective and efficient feature learning on these 2D slices. Based on the above two designs, we further introduce an SSM-based network for LFSR termed LFMamba. Experimental results on LF benchmarks demonstrate the superior performance of LFMamba. Furthermore, extensive ablation studies are conducted to validate the efficacy and generalization ability of our proposed method. We expect that our LFMamba shed light on effective representation learning of LFs with state space models. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Fast Calculation for Approximations in Dominance-Based Rough Set Approach Using Dual Information Granule

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SSRN

SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin See-To, Eric Wing-Kuen Huang, Faliang School of Management Guangdong University of Technology Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China

The Dominance-based Rough Set Approach (DRSA) is an extension of RST, which utilizes the dominance relation in attributes. However, traditional DRSA-based methods do not exploit the properties and logical structure in depth, which causes a high computational cost in lower and upper approximations. Besides, these methods are object-based, leading to repeated calculations from identical instances, and they involve numerous redundant computations to approximate different decisions. Therefore, we propose a novel approach, called DIGAC (Dual Information Granule-based Approximation Calculation), to improve DRSA by replacing the object-based calculation with the granule-based calculation. It effectively reduces the time complexity by constructing a granule-based ordered decision system. Additionally, this novel approach uses three types of Dual Information Granules (DIGs) to avoid repeated calculations from identical samples. In the process of lower and upper approximation, we leverage their transitivity to put forward a Distributed Storage-based Lower Approximation Calculation (DSLAC) strategy and a Query-based Upper Approximation Calculation (QUAC) strategy to eliminate redundant computations. We theoretically prove that the DIG-based approach extensively reduces the time complexity of the approximations and obtains the same approximations as the original counterpart. Our approach is investigated on 23 datasets, and the experimental results show that it outperforms existing algorithms in terms of efficiency and stability, especially for large-scale and high-dimensional datasets, where the average decrease in execution time is up to 99%. © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

End-to-end Open-vocabulary Video Visual Relationship Detection using Multi-modal Prompting

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arXiv 2024年

作者： Wang, Yongqi Yang, Shuo Wu, Xinxiao Luo, Jiebo The Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Guangdong Provincial Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Shenzhen518172 China The Beijing Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Computer Science University of Rochester RochesterNY14627 United States

Open-vocabulary video visual relationship detection aims to expand video visual relationship detection beyond annotated categories by detecting unseen relationships between both seen and unseen objects in videos. Existing methods usually use trajectory detectors trained on closed datasets to detect object trajectories, and then feed these trajectories into large-scale pre-trained vision-language models to achieve open-vocabulary classification. Such heavy dependence on the pre-trained trajectory detectors limits their ability to generalize to novel object categories, leading to performance degradation. To address this challenge, we propose to unify object trajectory detection and relationship classification into an end-to-end open-vocabulary framework. Under this framework, we propose a relationship-aware open-vocabulary trajectory detector. It primarily consists of a query-based Transformer decoder, where the visual encoder of CLIP is distilled for frame-wise open-vocabulary object detection, and a trajectory associator. To exploit relationship context during trajectory detection, a relationship query is embedded into the Transformer decoder, and accordingly, an auxiliary relationship loss is designed to enable the decoder to perceive the relationships between objects explicitly. Moreover, we propose an open-vocabulary relationship classifier that leverages the rich semantic knowledge of CLIP to discover novel relationships. To adapt CLIP well to relationship classification, we design a multi-modal prompting method that employs spatio-temporal visual prompting for visual representation and vision-guided language prompting for language input. Extensive experiments on two public datasets, VidVRD and VidOR, demonstrate the effectiveness of our framework. Our framework is also applied to a more difficult cross-dataset scenario to further demonstrate its generalization ability. © 2024, CC BY.

关键词： Decoding

Reinforcement-learning-based Algorithms for Optimization Problems and Applications to Inverse Problems

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arXiv 2023年

作者： Xu, Chen Zhao, Yun-Bin Lu, Zhipeng Zhang, Ye Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China Faculty of Computational Mathematics and Cybernetics Shenzhen MSU-BIT University Shenzhen518172 China School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China

We design a new iterative algorithm, called REINFORCE-OPT, for solving a general type of optimization problems. This algorithm parameterizes the solution search rule and iteratively updates the parameter using a reinforcement learning (RL) algorithm resembling REINFORCE. To gain a deeper understanding of the RL-based methods, we show that REINFORCE-OPT essentially solves a stochastic version of the given optimization problem, and that under standard assumptions, the searching rule parameter almost surely converges to a locally optimal value. Experiments show that REINFORCE-OPT outperforms other optimization methods such as gradient descent, the genetic algorithm, and particle swarm optimization, via its ability to escape from locally optimal solutions and its robustness to the choice of initial values. With rigorous derivations, we formally introduce the use of reinforcement learning to deal with inverse problems. By choosing specific probability models for the action-selection rule, we can also connect our approach to the conventional methods of Tikhonov regularization and iterative regularization. We take non-linear integral equations and parameter-identification problems in partial differential equations as examples to show how reinforcement learning can be applied in solving non-linear inverse problems. The numerical experiments highlight the strong performance of REINFORCE-OPT, as well as its ability to quantify uncertainty in error estimates and identify multiple solutions for ill-posed inverse problems that lack solution stability and *** Codes 90C26, 65J22, 65J20, 90C40, 90C15, 45Q05, 47A52 Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization algorithms

FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

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arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay