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

Deadline for manuscript submissions: 15 May 2023 | Viewed by 6987

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Special Issue Editor

Dr. Zichi Wang

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Guest Editor

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Interests: steganography; steganalysis; reversible data hiding; artificial intelligence security

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (7 papers)

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Research

Open AccessArticle

Image Inpainting Anti-Forensics Network via Attention-Guided Hierarchical Reconstruction

Liyun Dou

Guorui Feng

and

Zhenxing Qian

Symmetry 2023, 15(2), 393; https://doi.org/10.3390/sym15020393 - 02 Feb 2023

Viewed by 470

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 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

(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances)

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Open AccessArticle

A VVC Video Steganography Based on Coding Units in Chroma Components with a Deep Learning Network

Minghui Li

Zhaohong Li

and

Zhenzhen Zhang

Symmetry 2023, 15(1), 116; https://doi.org/10.3390/sym15010116 - 31 Dec 2022

Viewed by 445

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 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

(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances)

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Open AccessArticle

Intelligent Bio-Latticed Cryptography: A Quantum-Proof Efficient Proposal

Ohood Saud Althobaiti

Toktam Mahmoodi

and

Mischa Dohler

Symmetry 2022, 14(11), 2351; https://doi.org/10.3390/sym14112351 - 08 Nov 2022

Cited by 1 | Viewed by 564

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 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

(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances)

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Open AccessArticle

Deepfake Video Detection Based on MesoNet with Preprocessing Module

and

Symmetry 2022, 14(5), 939; https://doi.org/10.3390/sym14050939 - 05 May 2022

Cited by 2 | Viewed by 1224

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 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

(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances)

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Open AccessArticle

Multisource Data Hiding in Digital Images

Zichi Wang

Symmetry 2022, 14(5), 890; https://doi.org/10.3390/sym14050890 - 27 Apr 2022

Cited by 1 | Viewed by 745

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 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

(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances)

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Open AccessArticle

Perceptual Hash of Neural Networks

and

Symmetry 2022, 14(4), 810; https://doi.org/10.3390/sym14040810 - 13 Apr 2022

Cited by 1 | Viewed by 1052

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, 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

(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances)

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Open AccessArticle

Protecting the Intellectual Property of Speaker Recognition Model by Black-Box Watermarking in the Frequency Domain

Yumin Wang

and

Hanzhou Wu

Symmetry 2022, 14(3), 619; https://doi.org/10.3390/sym14030619 - 20 Mar 2022

Cited by 3 | Viewed by 1106

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 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

(This article belongs to the Special Issue Security, Communication and Privacy in Internet of Things: Symmetry and Advances)

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