Security, Communication and Privacy in Internet of Things: Symmetry and Advances

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 25178

Special Issue Editor

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Interests: steganography; steganalysis; reversible data hiding; artificial intelligence security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning has been well developed in recent years. The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task. During the employment of neural network models, the problems of intellectual property, communication overhead, and privacy protection appeared, including Multimedia and the Internet of Things. In the future, the above problems would be widely existed in the Internet of Things. It is valuable to focus on the security, communication, and privacy in symmetry application. This Special Issue aims to highlight and advance contemporary research on the security, communication, and privacy in Internet of Things: Symmetry and Advances. We invite contributions of both original research and reviews of research that organize the recent research results in a unified and systematic way.

Dr. Zichi Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • information security
  • communication and privacy
  • multimedia processing
  • Internet of Things
  • symmetry application
  • intellectual property

Published Papers (12 papers)

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Research

13 pages, 1717 KiB  
Article
Computation Offloading and Resource Allocation Based on Game Theory in Symmetric MEC-Enabled Vehicular Networks
by Keqin Zhang, Jianjie Yang and Zhijian Lin
Symmetry 2023, 15(6), 1241; https://doi.org/10.3390/sym15061241 - 10 Jun 2023
Cited by 3 | Viewed by 1375
Abstract
Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a [...] Read more.
Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods. Full article
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24 pages, 5569 KiB  
Article
Steganalysis of Neural Networks Based on Symmetric Histogram Distribution
by Xiong Tang, Zichi Wang and Xinpeng Zhang
Symmetry 2023, 15(5), 1079; https://doi.org/10.3390/sym15051079 - 13 May 2023
Cited by 1 | Viewed by 1231
Abstract
Deep neural networks have achieved remarkable success in various fields of artificial intelligence. However, these models, which contain a large number of parameters, are widely distributed and disseminated by researchers, engineers, and even unauthorized users. Except for intelligent tasks, typically overparameterized deep neural [...] Read more.
Deep neural networks have achieved remarkable success in various fields of artificial intelligence. However, these models, which contain a large number of parameters, are widely distributed and disseminated by researchers, engineers, and even unauthorized users. Except for intelligent tasks, typically overparameterized deep neural networks have become new digital covers for data hiding, which may pose significant security challenges to AI systems. To address this issue, this paper proposes a symmetric steganalysis scheme specifically designed for neural networks trained for image classification tasks. The proposed method focuses on detecting the presence of additional data without access to the internal structure or parameters of the host network. It employs a well-designed method based on histogram distribution to find the optimal decision threshold, with a symmetric determining rule where the original networks and stego networks undergo two highly symmetrical flows to generate the classification labels; the method has been shown to be practical and effective. SVM and ensemble classifiers were chosen as the binary classifier for their applicability to feature vectors output from neural networks based on different datasets and network structures. This scheme is the first of its kind, focusing on steganalysis for neural networks based on the distribution of network output, compared to conventional digital media such as images, audio, and video. Overall, the proposed scheme offers a promising approach to enhancing the security of deep neural networks against data hiding attacks. Full article
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15 pages, 8481 KiB  
Article
Enhanced Example Diffusion Model via Style Perturbation
by Haiyan Zhang and Guorui Feng
Symmetry 2023, 15(5), 1074; https://doi.org/10.3390/sym15051074 - 12 May 2023
Viewed by 1699
Abstract
With the extensive applications of neural networks in several fields, research on their security has become a hot topic. The digitization of paintings attracts our interest in the security of artistic style classification tasks. The concept of symmetry is commonly adopted in the [...] Read more.
With the extensive applications of neural networks in several fields, research on their security has become a hot topic. The digitization of paintings attracts our interest in the security of artistic style classification tasks. The concept of symmetry is commonly adopted in the construction of deep learning models. However, we find that low-quality artistic examples can fool high-performance deep neural networks. Therefore, we propose the enhanced example diffusion model (EDM) for low-quality paintings to symmetrically generate high-quality enhanced examples with positive style perturbations, which improves the performance of the deep learning-based style classification model. Our proposed framework consists of two parts: a style perturbation network that transforms the inputs into the latent space and extracts style features to form a positive style perturbation, and a conditional latent diffusion model that generates high-quality artistic features. High-quality artistic images are combined with positive style perturbations to generate artistic style-enhanced examples. We conduct extensive experiments on synthetic and real datasets, and find the effectiveness of our approach in improving the performance of deep learning models. Full article
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17 pages, 1345 KiB  
Article
Efficient Multi-Source Self-Attention Data Fusion for FDIA Detection in Smart Grid
by Yi Wu, Qiankuan Wang, Naiwang Guo, Yingjie Tian, Fengyong Li and Xiangjing Su
Symmetry 2023, 15(5), 1019; https://doi.org/10.3390/sym15051019 - 4 May 2023
Cited by 3 | Viewed by 1736
Abstract
As a new cyber-attack method in power cyber physical systems, false-data-injection attacks (FDIAs) mainly disturb the operating state of power systems by tampering with the measurement data of sensors, thereby avoiding bad-data detection by the power grid and threatening the security of power [...] Read more.
As a new cyber-attack method in power cyber physical systems, false-data-injection attacks (FDIAs) mainly disturb the operating state of power systems by tampering with the measurement data of sensors, thereby avoiding bad-data detection by the power grid and threatening the security of power systems. However, existing FDIA detection methods usually only focus on the detection feature extraction between false data and normal data, ignoring the feature correlation that easily produces diverse data redundancy, resulting in the significant difficulty of detecting false-data-injection attacks. To address the above problem, we propose a multi-source self-attention data fusion model for designing an efficient FDIA detection method. The proposed data fusing model firstly employs a temporal alignment technique to integrate the collected multi-source sensing data to the identical time dimension. Subsequently, a symmetric hybrid deep network model is built by symmetrically combining long short-term memory (LSTM) and a convolution neural network (CNN), which can effectively extract hybrid features for different multi-source sensing data. Furthermore, we design a self-attention module to further eliminate hybrid feature redundancy and aggregate the differences between attack-data features and normal-data features. Finally, the extracted features and their weights are integrated to implement false-data-injection attack detection using a single convolution operation. Extensive simulations are performed over IEEE14 node test systems and IEEE118 node test systems; the experimental results demonstrate that our model can achieve better data fusion effects and presents a superior detection performance compared with the state-of-the-art. Full article
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22 pages, 9689 KiB  
Article
Secure Steganographic Cover Generation via a Noise-Optimization Stacked StyleGAN2
by Jiang Yu, Xiaoyi Zhou, Wen Si, Fengyong Li, Cong Liu and Xinpeng Zhang
Symmetry 2023, 15(5), 979; https://doi.org/10.3390/sym15050979 - 25 Apr 2023
Cited by 1 | Viewed by 1196
Abstract
Recently, the style-based generative adversarial network StyleGAN2 yields state-of-art performance on unconditional high-quality image synthesis. However, from the perspective of steganography, the image security is not guaranteed during the image synthesis. Relying on the optimal properties of StyleGAN2, this paper proposes a noise-optimization [...] Read more.
Recently, the style-based generative adversarial network StyleGAN2 yields state-of-art performance on unconditional high-quality image synthesis. However, from the perspective of steganography, the image security is not guaranteed during the image synthesis. Relying on the optimal properties of StyleGAN2, this paper proposes a noise-optimization stacked StyleGAN2 named NOStyle to generate the secure and high-quality cover (image used for data hiding). In our proposed scheme, we decompose the image synthesis into two stages with symmetrical mode. In stage-I, StyleGAN2 is preserved to generate a high-quality benchmark image. In the stage-II generator, based on the progressive mechanism and shortcut connection, we design a noise secure optimization network by which the different-scale stochastic variation (noise map) is automatically adjusted according to the results of the stage-II discriminator. After injecting the stochastic variation into different resolutions of the synthesis network, the stage-II generator obtains an intermediate image. For the symmetrical stage-II discriminator, we combine the image secure loss and fidelity loss to construct the noise loss which is used to evaluate the difference between two images generated by the stage-I generator and stage-II generator. Taking the outputs of stage-II discriminator as inputs, by iteration, the stage-II generator finally creates the optimal image. Extensive experiments show that the generated image is not only secure but high quality. Moreover, we make a conclusion that the security of the generated image is inverse proportion to the fidelity. Full article
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15 pages, 8610 KiB  
Article
Image Inpainting Anti-Forensics Network via Attention-Guided Hierarchical Reconstruction
by Liyun Dou, Guorui Feng and Zhenxing Qian
Symmetry 2023, 15(2), 393; https://doi.org/10.3390/sym15020393 - 2 Feb 2023
Viewed by 1671
Abstract
Privacy security and property rights protection have gradually attracted the attention of people. Users not only hope that the images edited by themselves will not be forensically investigated, but also hope that the images they share will not be tampered with. Aiming at [...] Read more.
Privacy security and property rights protection have gradually attracted the attention of people. Users not only hope that the images edited by themselves will not be forensically investigated, but also hope that the images they share will not be tampered with. Aiming at the problem that inpainted images can be located by forensics, this paper proposes a general anti-forensics framework for image inpainting with copyright protection. Specifically, we employ a hierarchical attention model to symmetrically reconstruct the inpainting results based on existing deep inpainting methods. The hierarchical attention model consists of a structural attention stream and a texture attention stream in parallel, which can fuse hierarchical features to generate high-quality reconstruction results. In addition, the user’s identity information can be symmetrically embedded and extracted to protect copyright. The experimental results not only had high-quality structural texture information, but also had homologous features with the original region, which could mislead the detection of forensics analysis. At the same time, the protection of users’ privacy and property rights is also achieved. Full article
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15 pages, 2009 KiB  
Article
A VVC Video Steganography Based on Coding Units in Chroma Components with a Deep Learning Network
by Minghui Li, Zhaohong Li and Zhenzhen Zhang
Symmetry 2023, 15(1), 116; https://doi.org/10.3390/sym15010116 - 31 Dec 2022
Cited by 6 | Viewed by 1338
Abstract
Versatile Video Coding (VVC) is the latest video coding standard, but currently, most steganographic algorithms are based on High-Efficiency Video Coding (HEVC). The concept of symmetry is often adopted in deep neural networks. With the rapid rise of new multimedia, video steganography shows [...] Read more.
Versatile Video Coding (VVC) is the latest video coding standard, but currently, most steganographic algorithms are based on High-Efficiency Video Coding (HEVC). The concept of symmetry is often adopted in deep neural networks. With the rapid rise of new multimedia, video steganography shows great research potential. This paper proposes a VVC steganographic algorithm based on Coding Units (CUs). Considering the novel techniques in VVC, the proposed steganography only uses chroma CUs to embed secret information. Based on modifying the partition modes of chroma CUs, we propose four different embedding levels to satisfy the different needs of visual quality, capacity and video bitrate. In order to reduce the bitrate of stego-videos and improve the distortion caused by modifying them, we propose a novel convolutional neural network (CNN) as an additional in-loop filter in the VVC codec to achieve better restoration. Furthermore, the proposed steganography algorithm based on chroma components has an advantage in resisting most of the video steganalysis algorithms, since few VVC steganalysis algorithms have been proposed thus far and most HEVC steganalysis algorithms are based on the luminance component. Experimental results show that the proposed VVC steganography algorithm achieves excellent performance on visual quality, bitrate cost and capacity. Full article
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22 pages, 3219 KiB  
Article
Intelligent Bio-Latticed Cryptography: A Quantum-Proof Efficient Proposal
by Ohood Saud Althobaiti, Toktam Mahmoodi and Mischa Dohler
Symmetry 2022, 14(11), 2351; https://doi.org/10.3390/sym14112351 - 8 Nov 2022
Cited by 4 | Viewed by 1573
Abstract
The emergence of the Internet of Things (IoT) and the tactile internet presents high-quality connectivity strengthened by next-generation networking to cover a vast array of smart systems. Quantum computing is another powerful enabler of the next technological revolution, which will improve the world [...] Read more.
The emergence of the Internet of Things (IoT) and the tactile internet presents high-quality connectivity strengthened by next-generation networking to cover a vast array of smart systems. Quantum computing is another powerful enabler of the next technological revolution, which will improve the world tremendously, and it will continue to grow to cover an extensive array of important functions, in addition to it receiving recently great interest in the scientific scene. Because quantum computers have the potential to overcome various issues related to traditional computing, major worldwide technical corporations are investing competitively in them. However, along with its novel potential, quantum computing is introducing threats to cybersecurity algorithms, as quantum computers are able to decipher many complex mathematical problems that classical computers cannot. This research paper proposes a robust and performance-effective lattice-driven cryptosystem in the context of face recognition that provides lightweight intelligent bio-latticed cryptography, which will aid in overcoming the cybersecurity challenges of smart world applications in the pre- and post-quantum era and with sixth-generation (6G) networks. Since facial features are symmetrically used to generate encryption keys on the fly without sending or storing private data, our proposal has the valuable attribute of dramatically combining symmetric and asymmetric cryptography operations in the proposed cryptosystem. Implementation-based evaluation results prove that the proposed protocol maintains high-performance in the context of delay, energy consumption, throughput and stability on cellular network topology in classical Narrowband-Internet of Things (NB-IoT) mode. Full article
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14 pages, 1898 KiB  
Article
Deepfake Video Detection Based on MesoNet with Preprocessing Module
by Zhiming Xia, Tong Qiao, Ming Xu, Xiaoshuai Wu, Li Han and Yunzhi Chen
Symmetry 2022, 14(5), 939; https://doi.org/10.3390/sym14050939 - 5 May 2022
Cited by 16 | Viewed by 4958
Abstract
With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To [...] Read more.
With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%. Full article
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11 pages, 545 KiB  
Article
Multisource Data Hiding in Digital Images
by Zichi Wang
Symmetry 2022, 14(5), 890; https://doi.org/10.3390/sym14050890 - 27 Apr 2022
Cited by 2 | Viewed by 1322
Abstract
In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and [...] Read more.
In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and anonymous, according to different applications. In the separable scheme, the receiver can extract the secret data transmitted by all senders using the symmetrical data-hiding key. A sender is unable to know the content of the secret data that is not transmitted by them (non-source sender). In the anonymous scheme, it is unnecessary to extract all secret data on the receiver side. The content extracted by the receiver is a co-determined result of the secret data transmitted by all senders. Details of the secret data are unknown to the receiver and the non-source senders. In addition, the two proposed schemes achieve multisource data hiding without decreasing the undetectability of data hiding. Full article
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12 pages, 1419 KiB  
Article
Perceptual Hash of Neural Networks
by Zhiying Zhu, Hang Zhou, Siyuan Xing, Zhenxing Qian, Sheng Li and Xinpeng Zhang
Symmetry 2022, 14(4), 810; https://doi.org/10.3390/sym14040810 - 13 Apr 2022
Cited by 2 | Viewed by 2484
Abstract
In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, [...] Read more.
In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models. Full article
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10 pages, 287 KiB  
Article
Protecting the Intellectual Property of Speaker Recognition Model by Black-Box Watermarking in the Frequency Domain
by Yumin Wang and Hanzhou Wu
Symmetry 2022, 14(3), 619; https://doi.org/10.3390/sym14030619 - 20 Mar 2022
Cited by 12 | Viewed by 2261
Abstract
Benefiting from the rapid development of computer hardware and big data, deep neural networks (DNNs) have been widely applied in commercial speaker recognition systems, achieving a kind of symmetry between “machine-learning-as-a-service” providers and consumers. However, this symmetry is threatened by attackers whose goal [...] Read more.
Benefiting from the rapid development of computer hardware and big data, deep neural networks (DNNs) have been widely applied in commercial speaker recognition systems, achieving a kind of symmetry between “machine-learning-as-a-service” providers and consumers. However, this symmetry is threatened by attackers whose goal is to illegally steal and use the service. It is necessary to protect these DNN models from symmetry breaking, i.e., intellectual property (IP) infringement, which motivated the authors to present a black-box watermarking method for IP protection of the speaker recognition model in this paper. The proposed method enables verification of the ownership of the target marked model by querying the model with a set of carefully crafted trigger audio samples, without knowing the internal details of the model. To achieve this goal, the proposed method marks the host model by training it with normal audio samples and carefully crafted trigger audio samples. The trigger audio samples are constructed by adding a trigger signal in the frequency domain of normal audio samples, which enables the trigger audio samples to not only resist against malicious attack but also avoid introducing noticeable distortion. In order to not impair the performance of the speaker recognition model on its original task, a new label is assigned to all the trigger audio samples. The experimental results show that the proposed black-box DNN watermarking method can not only reliably protect the intellectual property of the speaker recognition model but also maintain the performance of the speaker recognition model on its original task, which verifies the superiority and maintains the symmetry between “machine-learning-as-a-service” providers and consumers. Full article
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