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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

MC-GAN: An Adversarial Sample Defense Algorithm 20

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MC-GAN: An Adversarial Sample Defense Algorithm

20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chenli, Wang Juyang, W.U. Xing, Yang Junfei, Wang Jian, S.H.U. Jiazhong, L.U. Yuanyuan, Huang Shuo, H.A.N. School of Cybersecurity Chengdu University of Information Technology Sichuan Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan Chengdu610225 China Northwestern University Department of Statistics and Data Sciences EvanstonIL60202 United States

ISBN: (纸本)9798350318982

The current adversarial sample defense algorithms are plagued by issues such as poor defense effectiveness and high training costs. In order to enhance sample classification accuracy while reducing training costs, we have introduced a novel defense algorithm based on model compression within the framework of Generative Adversarial Networks (GANs). This algorithm integrates GANs with model compression techniques, and the results of testing on public datasets have demonstrated an improved accuracy in defending against adversarial samples along with reduced training costs. © 2023 IEEE.

关键词： Generative adversarial networks

Enhancing the MILP/MIQCP-based Automatic Search for Differential-Linear Distinguishers of Simon-Like Ciphers

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arXiv 2024年

作者： Chen, Siwei Xiang, Zejun Zeng, Xiangyong Qin, Guangxue School of Cyber Science and Technology Key Laboratory of Intelligent Sensing System and Security Hubei University Wuhan China Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University Wuhan China

In this paper, we propose an improved method based on Mixed-Integer Linear Programming/Mixed-Integer Quadratic Constraint Programming (MILP/MIQCP) to automatically find better differential-linear (DL) distinguishers for the all members of Simon and Simeck block cipher families. To be specific, we first give the completely precise MILP model to describe the linear part, and explain how to utilize the general expressions of Gurobi solver to model the propagation of continuous difference for the middle part in a quite easy way. Secondly, in order to solve the MILP/MIQCP model in a reasonable time, we propose two heuristic strategies based on the divide-and-conquer idea to speed up the search process. Thirdly, we introduce the transforming technique, which exploits the clustering effect on DL trails, to improve the estimated correlation of the DL approximation. We apply our method to Simon and Simeck block cipher families. Consequently, we find the 14/17/21/26-round theoretical DL distinguishers of Simon 32/48/64/96, which extend the previous longest ones of Simon 32/48/96 by one round and Simon 64 by two rounds, respectively. For Simeck, we do not explore longer distinguishers compared to the currently best results, but refresh all the results of Zhou et al. (the first work to automate finding DL distinguishers for Simon-like ciphers using MILP/MIQCP). Besides, in order to validate the correctness of these distinguishers, the experimental verifications are conducted on Simon 32/Simeck 32 and Simon 48/Simeck 48. The results show that our theoretical estimations on correlations are very close to the experimental ones, which can be regarded as a concrete support for the effectiveness of our method. © 2024, CC BY.

关键词： Mixed-integer linear programming

Poisoned LangChain: Jailbreak LLMs by LangChain

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arXiv 2024年

作者： Wang, Ziqiu Liu, Jun Zhang, Shengkai Yang, Yang Wuhan University of Technology Wuhan China Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Hubei University Wuhan China

With the development of Natural Language Processing (NLP), Large Language Models (LLMs) are becoming increasingly popular. LLMs are integrating more into everyday life, raising public concerns about their security vulnerabilities. Consequently, the security of large language models is becoming critically important. Currently, the techniques for attacking and defending against LLMs are continuously evolving. One significant method type of attack is the jailbreak attack, which designed to evade model safety mechanisms and induce the generation of inappropriate content. Existing jailbreak attacks primarily rely on crafting inducement prompts for direct jailbreaks, which are less effective against large models with robust filtering and high comprehension abilities. Given the increasing demand for real-time capabilities in large language models, real-time updates and iterations of new knowledge have become essential. Retrieval-Augmented Generation (RAG), an advanced technique to compensate for the model’s lack of new knowledge, is gradually becoming mainstream. As RAG enables the model to utilize external knowledge bases, it provides a new avenue for jailbreak attacks. In this paper, we conduct the first work to propose the concept of indirect jailbreak and achieve Retrieval-Augmented Generation via LangChain. Building on this, we further design a novel method of indirect jailbreak attack, termed Poisoned-LangChain (PLC), which leverages a poisoned external knowledge base to interact with large language models, thereby causing the large models to generate malicious non-compliant *** tested this method on six different large language models across three major categories of jailbreak issues. The experiments demonstrate that PLC successfully implemented indirect jailbreak attacks under three different scenarios, achieving success rates of 88.56%, 79.04%, and 82.69% respectively. Experimental results and other resources: https://***/CAM-FSS/jailbreak-langchai

关键词： Knowledge based systems

Dlr: Adversarial Examples Detection and Label Recovery for Deep Neural Networks

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SSRN

SSRN 2023年

作者： Han, Keji Ge, Yao Wang, Ruchuan Li, Yun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

Deep neural networks (DNNs) are demonstrated to be vulnerable to the adversarial example crafted by the adversary to fool the target model. Adversarial training and adversarial example detection are two popular methods to mitigate the adversarial issue. The adversarial training makes the target model recognize the adversarial example correctly for the image classification task, while it does not generalize well. The adversarial detection generalizes well but cannot help the target model recognize the adversarial example. We first define the label recovery task to address the adversarial issue for DNNs. Then we propose a new generative classifier and apply it to the adversarial example label recovery task. The proposed method is named as Detection and Label Recovery (DLR) defense method, which consists of Detector and Recover. Detector feeds the legitimate and adversarial examples to the target model and Recover, respectively. The Recover explores the ground-truth label of the detected adversarial example. DLR combines the merits of adversarial training and adversarial example detection. Experimental results demonstrate that our method outperforms some state-of-the-art adversarial training methods. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

Current density in the anomalous Hall effect regime under weak scattering

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arXiv 2024年

作者： Dai, Ning Zhou, Bin Department of Physics Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan430062 China

A finite equilibrium current density arises in the anomalous Hall effect (AHE) as a result of time-reversal symmetry breaking, affecting both the differential current density and total current. This study illustrates the equilibrium current density pattern in a ribbon-shaped system within the AHE regime, consisting of two sets of counterpropagating channels arranged in a zebra crossing pattern. While the middle channels are susceptible to scattering, the edge channels remain relatively robust. Despite this difference, all channels exhibit the same differential current density when subjected to a differential voltage across the two ends of the ribbon. When a differential voltage is applied to both sides of the ribbon, it results in a snaking pattern of differential current density forming across it. Furthermore, in a four-terminal device comprising an AHE ribbon and two normal leads, it is found that Hall conductance is independent of ribbon width within certain scattering strengths due to differences in robustness between middle and edge channels. These findings underscore the significant role played by current density in AHE transport. Copyright © 2024, The Authors. All rights reserved.

关键词： Hall effect devices

Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction

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arXiv 2024年

作者： Qiao, Xiaoyu Li, Weisheng Xiao, Bin Huang, Yuping Yang, Lijian Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

Score matching with Langevin dynamics (SMLD) method has been successfully applied to accelerated MRI. However, the hyperparameters in the sampling process require subtle tuning, otherwise the results can be severely corrupted by hallucination artifacts, especially with out-of-distribution test data. To address the limitations, we proposed a novel workflow where naive SMLD samples serve as additional priors to guide model-driven network training. First, we adopted a pretrained score network to generate samples as preliminary guidance images (PGI), obviating the need for network retraining, parameter tuning and in-distribution test data. Although PGIs are corrupted by hallucination artifacts, we believe they can provide extra information through effective denoising steps to facilitate reconstruction. Therefore, we designed a denoising module (DM) in the second step to coarsely eliminate artifacts and noises from PGIs. The features are extracted from a score-based information extractor (SIE) and a cross-domain information extractor (CIE), which directly map to the noise patterns. Third, we designed a model-driven network guided by denoised PGIs (DGIs) to further recover fine details. DGIs are densely connected with intermediate reconstructions in each cascade to enrich the information and are periodically updated to provide more accurate guidance. Our experiments on different datasets reveal that despite the low average quality of PGIs, the proposed workflow can effectively extract valuable information to guide the network training, even with severely reduced training data and sampling steps. Our method outperforms other cutting-edge techniques by effectively mitigating hallucination artifacts, yielding robust and high-quality reconstruction results. Copyright © 2024, The Authors. All rights reserved.

关键词： Compressed sensing

Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

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Using Depth-Enhanced Spatial Transformation for Student Gaze...

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering

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arXiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms