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Information Theory and Its Applications in Multimedia Security and Processing

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 22769

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Prof. Dr. James C.N. Yang

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Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien 97401, Taiwan
Interests: information security; coding theory; cryptography; data hiding; steganography
Special Issues, Collections and Topics in MDPI journals

Dr. Stelvio Cimato

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Department of Computer Science, Università degli studi di Milano, 20122 Milan, Italy
Interests: secure computation; security on cloud computing; web services security
Special Issues, Collections and Topics in MDPI journals

Dr. Lizhi Xiong

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School of Computer and Software, Nanjing University of Information Science and Technology, 210044 Nanjing, China
Interests: cloud computing; multimedia security; data hiding; artificial intelligence

Special Issue Information

Dear Colleagues,

Information theory has contributed to mathematical and statistical modeling in many research fields. In this Special Issue, we focus on multimedia security and processing. With the increasing popularity of smart devices and 5G networks in our daily life, multimedia data acquisition, storage and processing on the Internet have become increasingly convenient. However, this causes more security issues, such as privacy leakage, data tampering, and forgery attacks, which have received a lot of attention. Meanwhile, the processing of multimedia data, e.g., the compression and coding of sound, image, and video information, should also be more theoretically studied. Therefore, we believe that the issues of multimedia security and processing will be improved from the perspective of using information theory and applications.

For example, coding theory is usually used for data compression, cryptography, error detection and correction, data transmission and data storage. Regarding multimedia security and processing, coding theory involves the removal of redundancy and the correction or detection of errors in the transmitted data. In addition, new artificial intelligence technologies, such as convolutional neural networks (CNNs) and generative adversarial networks (GAN), have achieved great success in computer vision tasks; it might be promising to adopt these artificial intelligence technologies to address the issues of the removal of redundancy and the correction or detection of errors. The above theories and techniques can better support the development of multimedia security and processing.

In this Special Issue, we hope to share the research achievements of key researchers and practitioners in academia, as well as the industry, dealing with a wide range of theoretical and applied problems in the field of multimedia. The aim of this Special Issue is to collect papers dealing with information theory and applications in multimedia security and processing. In addition to submissions on coding theory, cryptography and deep learning, we solicit original research addressing multimedia security and processing via information theory and applications. Authors are encouraged to submit contributions based on, but not limited to, the following potential topics:

Information modeling and analysis of multimedia data security;
Information modeling and analysis of cloud security;
Information modeling and analysis of multimedia data compression;
Information modeling and analysis of multimedia data coding;
Code-based steganography/data hiding and its applications;
Code-based watermarking and its applications;
Code-based secret multimedia sharing and its applications.

Prof. Dr. James Yang
Dr. Stelvio Cimato
Dr. Lizhi Xiong
Guest Editors

Manuscript Submission Information

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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. Entropy 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 2600 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

information theory
coding theory
cryptography
information security
multimedia processing

Published Papers (12 papers)

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Research

16 pages, 9429 KiB

Open AccessArticle

Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model

by Wei Gu, Ching-Chun Chang, Yu Bai, Yunyuan Fan, Liang Tao and Li Li

Entropy 2023, 25(2), 288; https://doi.org/10.3390/e25020288 - 03 Feb 2023

Cited by 1 | Viewed by 1781

Abstract

Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM “ScreenNet”. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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22 pages, 2000 KiB

Open AccessArticle

Low Computational Coding-Efficient Distributed Video Coding: Adding a Decision Mode to Limit Channel Coding Load

by Shahzad Khursheed, Nasreen Badruddin, Varun Jeoti, Dejan Vukobratovic and Manzoor Ahmed Hashmani

Entropy 2023, 25(2), 241; https://doi.org/10.3390/e25020241 - 28 Jan 2023

Viewed by 1022

Abstract

Distributed video coding (DVC) is based on distributed source coding (DSC) concepts in which video statistics are used partially or completely at the decoder rather than the encoder. The rate-distortion (RD) performance of distributed video codecs substantially lags the conventional predictive video coding. Several techniques and methods are employed in DVC to overcome this performance gap and achieve high coding efficiency while maintaining low encoder computational complexity. However, it is still challenging to achieve coding efficiency and limit the computational complexity of the encoding and decoding process. The deployment of distributed residual video coding (DRVC) improves coding efficiency, but significant enhancements are still required to reduce these gaps. This paper proposes the QUAntized Transform ResIdual Decision (QUATRID) scheme that improves the coding efficiency by deploying the Quantized Transform Decision Mode (QUAM) at the encoder. The proposed QUATRID scheme’s main contribution is a design and integration of a novel QUAM method into DRVC that effectively skips the zero quantized transform (QT) blocks, thus limiting the number of input bit planes to be channel encoded and consequently reducing both the channel encoding and decoding computational complexity. Moreover, an online correlation noise model (CNM) is specifically designed for the QUATRID scheme and implemented at its decoder. This online CNM improves the channel decoding process and contributes to the bit rate reduction. Finally, a methodology for the reconstruction of the residual frame (

\hat{R}

) is developed that utilizes the decision mode information passed by the encoder, decoded quantized bin, and transformed estimated residual frame. The Bjøntegaard delta analysis of experimental results shows that the QUATRID achieves better performance over the DISCOVER by attaining the PSNR between 0.06 dB and 0.32 dB and coding efficiency, which varies from 5.4 to 10.48 percent. In addition to this, results determine that for all types of motion videos, the proposed QUATRID scheme outperforms the DISCOVER in terms of reducing the number of input bit-planes to be channel encoded and the entire encoder’s computational complexity. The number of bit plane reduction exceeds 97%, while the entire Wyner-Ziv encoder and channel coding computational complexity reduce more than nine-fold and 34-fold, respectively. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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23 pages, 9961 KiB

Open AccessArticle

An Image Encryption Algorithm Based on Complex Network Scrambling and Multi-Directional Diffusion

by Yaohui Sheng, Jinqing Li, Xiaoqiang Di, Xusheng Li and Rui Xu

Entropy 2022, 24(9), 1247; https://doi.org/10.3390/e24091247 - 05 Sep 2022

Cited by 4 | Viewed by 1739

Abstract

Various security threats are encountered when keys are transmitted in public channels. In this paper, we propose an image encryption algorithm based on complex network scrambling and multi-directional diffusion. Combining the idea of public key cryptography, the RSA algorithm is used to encrypt the key related to plaintext. The algorithm consists of three stages: key generation stage, complex network scrambling stage, and multi-directional diffusion stage. Firstly, during the key generation phase, SHA-512 and the original image are used to generate plaintext-related information, which is then converted to plaintext-related key through transformation mapping. Secondly, in the complex network scrambling stage, the chaotic random matrix establishes the node relationships in the complex network, which is then used to construct an image model based on the complex network, and then combines pixel-level and block-level methods to scramble images. Finally, in the multi-directional diffusion stage, the multi-directional diffusion method is used to perform forward diffusion, middle spiral diffusion, and backward diffusion on the image in turn to obtain the final ciphertext image. The experimental results show that our encryption algorithm has a large keyspace, the encrypted image has strong randomness and robustness, and can effectively resist brute force attack, statistical attack, and differential attack. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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25 pages, 6285 KiB

Open AccessArticle

QuatJND: A Robust Quaternion JND Model for Color Image Watermarking

by Wenbo Wan, Wenqing Li, Wenxiu Liu, Zihan Diao and Yantong Zhan

Entropy 2022, 24(8), 1051; https://doi.org/10.3390/e24081051 - 30 Jul 2022

Cited by 2 | Viewed by 1241

Abstract

Robust quantization watermarking with perceptual JND model has made a great success for image copyright protection. Generally, either restores each color channel separately or processes the vector representation from three color channels with the traditional monochromatic model. And it cannot make full use of the high correlation among RGB channels. In this paper, we proposed a robust quaternion JND Model for color image watermarking (QuatJND). In contrast to the existing perceptual JND models, the advantage of QuatJND is that it can integrate quaternion representation domain and colorfulness simultaneously, and QuatJND incorporates the pattern guided contrast masking effect in quaternion domain. On the other hand, in order to efficiently utilize the color information, we further develop a robust quantization watermarking framework using the color properties of the quaternion DCT coefficients in QuatJND. And the quantization steps of each quaternion DCT block in the scheme are optimal. Experimental results show that our method has a good performance in term of robustness with better visual quality. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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16 pages, 745 KiB

Open AccessArticle

MLD: An Intelligent Memory Leak Detection Scheme Based on Defect Modes in Software

by Ling Yuan, Siyuan Zhou, Peng Pan and Zhenjiang Wang

Entropy 2022, 24(7), 947; https://doi.org/10.3390/e24070947 - 07 Jul 2022

Viewed by 1981

Abstract

With the expansion of the scale and complexity of multimedia software, the detection of software defects has become a research hotspot. Because of the large scale of the existing software code, the efficiency and accuracy of the existing software defect detection algorithms are relatively low. We propose an intelligent memory leak detection scheme MLD based on defect modes in software. Based on the analysis of existing memory leak defect modes, we summarize memory operation behaviors (allocation, release and transfer) and present a state machine model. We employ a fuzzy matching algorithm based on regular expression to determine the memory operation behaviors and then analyze the change in the state machine to assess the vulnerability in the source code. To improve the efficiency of detection and solve the problem of repeated detection at the function call point, we propose a function summary method for memory operation behaviors. The experimental results demonstrate that the method we proposed has high detection speed and accuracy. The algorithm we proposed can identify the defects of the software, reduce the risk of being attacked to ensure safe operation. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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25 pages, 5768 KiB

Open AccessArticle

VQProtect: Lightweight Visual Quality Protection for Error-Prone Selectively Encrypted Video Streaming

by Syeda Maria Gillani, Mamoona Naveed Asghar, Amna Shifa, Saima Abdullah, Nadia Kanwal and Martin Fleury

Entropy 2022, 24(6), 755; https://doi.org/10.3390/e24060755 - 26 May 2022

Cited by 1 | Viewed by 1650

Abstract

Mobile multimedia communication requires considerable resources such as bandwidth and efficiency to support Quality-of-Service (QoS) and user Quality-of-Experience (QoE). To increase the available bandwidth, 5G network designers have incorporated Cognitive Radio (CR), which can adjust communication parameters according to the needs of an application. The transmission errors occur in wireless networks, which, without remedial action, will result in degraded video quality. Secure transmission is also a challenge for such channels. Therefore, this paper’s innovative scheme “VQProtect” focuses on the visual quality protection of compressed videos by detecting and correcting channel errors while at the same time maintaining video end-to-end confidentiality so that the content remains unwatchable. For the purpose, a two-round secure process is implemented on selected syntax elements of the compressed H.264/AVC bitstreams. To uphold the visual quality of data affected by channel errors, a computationally efficient Forward Error Correction (FEC) method using Random Linear Block coding (with complexity of

O (k (n - 1)

) is implemented to correct the erroneous data bits, effectively eliminating the need for retransmission. Errors affecting an average of 7–10% of the video data bits were simulated with the Gilbert–Elliot model when experimental results demonstrated that 90% of the resulting channel errors were observed to be recoverable by correctly inferring the values of erroneous bits. The proposed solution’s effectiveness over selectively encrypted and error-prone video has been validated through a range of Video Quality Assessment (VQA) metrics. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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16 pages, 2483 KiB

Open AccessArticle

MTS-Stega: Linguistic Steganography Based on Multi-Time-Step

by Long Yu, Yuliang Lu, Xuehu Yan and Yongqiang Yu

Entropy 2022, 24(5), 585; https://doi.org/10.3390/e24050585 - 22 Apr 2022

Cited by 2 | Viewed by 1782

Abstract

Generative linguistic steganography encodes candidate words with conditional probability when generating text by language model, and then, it selects the corresponding candidate words to output according to the confidential message to be embedded, thereby generating steganographic text. The encoding techniques currently used in generative text steganography fall into two categories: fixed-length coding and variable-length coding. Because of the simplicity of coding and decoding and the small computational overhead, fixed-length coding is more suitable for resource-constrained environments. However, the conventional text steganography mode selects and outputs a word at one time step, which is highly susceptible to the influence of confidential information and thus may select words that do not match the statistical distribution of the training text, reducing the quality and concealment of the generated text. In this paper, we inherit the decoding advantages of fixed-length coding, focus on solving the problems of existing steganography methods, and propose a multi-time-step-based steganography method, which integrates multiple time steps to select words that can carry secret information and fit the statistical distribution, thus effectively improving the text quality. In the experimental part, we choose the GPT-2 language model to generate the text, and both theoretical analysis and experiments prove the effectiveness of the proposed scheme. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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23 pages, 7455 KiB

Open AccessArticle

ABCAttack: A Gradient-Free Optimization Black-Box Attack for Fooling Deep Image Classifiers

by Han Cao, Chengxiang Si, Qindong Sun, Yanxiao Liu, Shancang Li and Prosanta Gope

Entropy 2022, 24(3), 412; https://doi.org/10.3390/e24030412 - 15 Mar 2022

Cited by 3 | Viewed by 2156

Abstract

The vulnerability of deep neural network (DNN)-based systems makes them susceptible to adversarial perturbation and may cause classification task failure. In this work, we propose an adversarial attack model using the Artificial Bee Colony (ABC) algorithm to generate adversarial samples without the need for a further gradient evaluation and training of the substitute model, which can further improve the chance of task failure caused by adversarial perturbation. In untargeted attacks, the proposed method obtained 100%, 98.6%, and 90.00% success rates on the MNIST, CIFAR-10 and ImageNet datasets, respectively. The experimental results show that the proposed ABCAttack can not only obtain a high attack success rate with fewer queries in the black-box setting, but also break some existing defenses to a large extent, and is not limited by model structure or size, which provides further research directions for deep learning evasion attacks and defenses. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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26 pages, 1433 KiB

Open AccessArticle

Minimum Adversarial Examples

by Zhenyu Du, Fangzheng Liu and Xuehu Yan

Entropy 2022, 24(3), 396; https://doi.org/10.3390/e24030396 - 12 Mar 2022

Cited by 1 | Viewed by 1723

Abstract

Deep neural networks in the area of information security are facing a severe threat from adversarial examples (AEs). Existing methods of AE generation use two optimization models: (1) taking the successful attack as the objective function and limiting perturbations as the constraint; (2) taking the minimum of adversarial perturbations as the target and the successful attack as the constraint. These all involve two fundamental problems of AEs: the minimum boundary of constructing the AEs and whether that boundary is reachable. The reachability means whether the AEs of successful attack models exist equal to that boundary. Previous optimization models have no complete answer to the problems. Therefore, in this paper, for the first problem, we propose the definition of the minimum AEs and give the theoretical lower bound of the amplitude of the minimum AEs. For the second problem, we prove that solving the generation of the minimum AEs is an NPC problem, and then based on its computational inaccessibility, we establish a new third optimization model. This model is general and can adapt to any constraint. To verify the model, we devise two specific methods for generating controllable AEs under the widely used distance evaluation standard of adversarial perturbations, namely

L_{p}

constraint and

S S I M

constraint (structural similarity). This model limits the amplitude of the AEs, reduces the solution space’s search cost, and is further improved in efficiency. In theory, those AEs generated by the new model which are closer to the actual minimum adversarial boundary overcome the blindness of the adversarial amplitude setting of the existing methods and further improve the attack success rate. In addition, this model can generate accurate AEs with controllable amplitude under different constraints, which is suitable for different application scenarios. In addition, through extensive experiments, they demonstrate a better attack ability under the same constraints as other baseline attacks. For all the datasets we test in the experiment, compared with other baseline methods, the attack success rate of our method is improved by approximately 10%. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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10 pages, 3865 KiB

Open AccessArticle

Splitting an Arbitrary Three-Qubit State via a Five-Qubit Cluster State and a Bell State

by Gang Xu, Tianai Zhou, Xiu-Bo Chen and Xiaojun Wang

Entropy 2022, 24(3), 381; https://doi.org/10.3390/e24030381 - 08 Mar 2022

Cited by 4 | Viewed by 1861

Abstract

Quantum information splitting (QIS) provides an idea for transmitting the quantum state through a classical channel and a preshared quantum entanglement resource. This paper presents a new scheme for QIS based on a five-qubit cluster state and a Bell state. In this scheme, the sender transmits the unknown three-qubit secret state to two agents by the quantum channel with the Bell basis measurement three times and broadcasts the measurement results to the agents through the classical channel. The agent who restores the secret state can successfully recover the initial information to be transmitted through the appropriate unitary operation with the help of the other party. Firstly, our scheme’s process can be accurately realized by performing the applicable Bell basis measurement, single-qubit measurement, and local unitary operation instead of a multiparticle joint measurement. The splitting process of quantum information is realized through a convenient operation. Secondly, compared with some previous schemes, the efficiency of the total scheme has been improved in principle, and the qubit consumption is reduced. Finally, the security of the quantum information splitting scheme is analyzed from the perspectives of external attacks and participant attacks. It is proved that our scheme can effectively resist internal participant attacks and external eavesdropper attacks. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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12 pages, 11856 KiB

Open AccessArticle

Meaningful Secret Image Sharing with Saliency Detection

by Jingwen Cheng, Xuehu Yan, Lintao Liu, Yue Jiang and Xuan Wang

Entropy 2022, 24(3), 340; https://doi.org/10.3390/e24030340 - 26 Feb 2022

Cited by 8 | Viewed by 2192

Abstract

Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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29 pages, 4149 KiB

Open AccessArticle

A Novel General (n, n)-Threshold Multiple Secret Images Sharing Scheme Based on Information Hiding in the Sharing Domain

by Fengyue Xing, Xuehu Yan, Long Yu and Longlong Li

Entropy 2022, 24(3), 318; https://doi.org/10.3390/e24030318 - 23 Feb 2022

Cited by 2 | Viewed by 1321

Abstract

(k, n)

(k, n)

-threshold secret image sharing (SIS) protects an image by dividing it into n shadow images. The secret image will be recovered as we gather k or more shadow images. In complex networks, the security, robustness and efficiency of protecting images draws more and more attention. Thus, we realize multiple secret images sharing (MSIS) by information hiding in the sharing domain (IHSD) and propose a novel and general

(n, n)

-threshold IHSD-MSIS scheme (IHSD-MSISS), which can share and recover two secret images simultaneously. The proposed scheme spends less cost on managing and identifying shadow images, and improves the ability to prevent malicious tampering. Moreover, it is a novel approach to transmit important images with strong associations. The superiority of

(n, n)

-threshold IHSD-MSISS is in fusing the sharing phases of two secret images by controlling randomness of SIS. We present a general construction model and algorithms of the proposed scheme. Sufficient theoretical analyses, experiments and comparisons show the effectiveness of the proposed scheme. Full article

(This article belongs to the Special Issue Information Theory and Its Applications in Multimedia Security and Processing)

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