Multimedia Image Research Articles

Selective encryption of JPEG images with chaotic based novel S-box

Increased demand of multimedia data over heterogeneous networks has led to the requirement for increased compression and suitable security. Both of these demands require heavy computation, and are contradictory to each other, as encryption may severely affect the compression efficiency. On the other hand, handheld devices have limited energy and computational resources, making compression friendly encryption a challenging issue. One of the solutions is to encrypt a subset of data based on perceptual importance. But, that too may not be compression efficient, format compliant and secure. To address the security issue in such applications the substitution box (S-box), which is one of the most important and the only non-linear operation of the block ciphers is redesigned using chaotic equations. These equations have the characteristics that easily meet the requirements of the block ciphers. In this research work, the S-box based on the chaotic equation was designed and tested for security strength and then used for selective encryption of the multimedia (image) data. To increase the security and generate a key dependant S-box, Mackey Glass equation is used due to its inherent properties like sensitivity, pseudo random characteristics, non-uniform behaviour, ergodicity, and high confusion and diffusion. The security of the proposed S-box was tested in terms of non-linearity, Bit Independence and Strict Avalanche Criteria etc. This S-box is then used to selectively encrypt a subset of image data during the process of compression, giving an encryption-compression strategy. The subset of image data was selected such that the statistical and structural dependencies were not violated due to encryption. This resulted in compression efficient and format friendly encryption. In this regard the quantization table is selected for encryption along with a subset of bits that have already encoded and their statistical dependencies are exploited for compression. This approach not only reduced the computational burden due to the selective nature of encryption but also kept the security at the required level due to introduction of key dependent S-Box. The results of encrypted images were compared with that of the AES in terms of compression ratio, correlation and Peak Signal to Noise Ratio (PSNR), giving better results for the proposed algorithm.

Image compression using adaptive multiresolution image decomposition algorithm

With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches.

Multimedia image and video retrieval based on an improved HMM

In today's information age, information is gathered from text and more complex media, such as images, audio, and video. Among these data sources, the rapid growth of video information has led to it to gradually become the main source of information in people's lives. Video information is characterized by many kinds of information, complex forms, and a low degree of structure. Therefore, effectively classifying, managing and retrieving video information has become a difficult problem to solve. In this paper, an improved crow search algorithm is used to process video images, and the information entropy is used to extract the key frames, which reduces the computation burden of each frame feature calculation and feature contrast process, thus shortening the key frame detection time. Then, all the feature sets are extracted and used as input for an HMM according to the observed sequence $$O = O_{1} ,O_{2} ,O_{3} , \cdot \cdot \cdot ,O_{T}$$ of the input image or video data and the initial model parameters $$\lambda = (\pi ,A,B)$$ . According to the training rules, the model parameters are repeatedly adjusted and modified, and the new model $$\overline{\lambda }$$ is constructed step by step to realize the retrieval of multimedia images and videos. The experimental results show that the method has obvious advantages in terms of the retrieval time and retrieval effect and provides new ideas for multimedia image and video retrieval.

Symmetric keys image encryption and decryption using 3D chaotic maps with DNA encoding technique

In present digital era, multimedia like images, text, documents and videos plays a vital role, therefore due to increase in usage of digital data; there comes high demand of security. Encryption is a technique used to secure and protect the images from unfair means. In cryptography, chaotic maps play an important role in forming strong and effective encryption algorithm. In this paper 3D chaotic logistic map with DNA encoding is used for confusion and diffusion of image pixels. Additionally, three symmetric keys are used to initialize 3D chaos logistic map, which makes the encryption algorithm strong. The symmetric keys used are 32 bit ASCII key, Chebyshev chaotic key and prime key. The algorithm first applies 3D non-linear logistic chaotic map with three symmetric keys in order to generate initial conditions. These conditions are then used in image row and column permutation to create randomness in pixels. The third chaotic sequence generated by 3D map is used to generate key image. Diffusion of these random pixels are done using DNA encoding; further XOR logical operation is applied between DNA encoded input image and key image. Analysis parameters like NPCR, UACI, entropy, histogram, chi-square test and correlation are calculated for proposed algorithm and also compared with different existing encryption methods.

Drone Forensics: Investigative Guide for Law Enforcement Agencies

Nowadays, sophisticated criminals are using drones in different ways like spying, bombarding, shooting, etc. Law enforcement agencies have to deal with the biggest challenges to cope up and to investigate such incidents from the drones found from the crime scene. Drones used in crimes can provide valuable sources of information by analysing storage media and logs on drones, including flying routes and their take-off source and landing destinations as well as multimedia like images and videos taken by drones, whom have controlled and remotely operated it, the controller ID and metadata. In this research, we are going to discuss how such incidents can be investigated through forensics procedures. In which, the investigator can perform drone acquisition, evidence collection, forensic investigation, and reporting. This will cover the general legal procedure to collect and analyse any drones from the crime scene and investigate inside the lab.

Effectiveness of educational video in improving oral hygiene in preschool students

Introduction: In recent years, attention has been drawn toward assessing the effectiveness of an oral health education program, especially for preschool students, in order to improve oral hygiene status. There is a difference in the level of ability and learning methods based on age. For early childhood, they tend to imitate the things they see, both people and multimedia images. This research was aimed to analyse the effectiveness of educational video in improving oral hygiene level in preschool students. Methods: This type of research was quasi-experimental with one group pretest-posttest design. The sampling method was the total sampling and obtained 40 subjects. The research population was all students in Al Azhar Kindergarten aged 4-6 years and met the inclusion criteria. Greene and Vermilion Plaque Index was applied to measure the oral hygiene status of each subject before and after the intervention with educational video. The intervention consisted of four meetings; each meeting consisted of before and after plaque index measurement and also after watching the educational video. Data were analysed using t-paired statistical analysis. Results: The result showed a comparison of difference of the plaque index value change on the day I with day VII, which obtained the t-value = 3.365, higher than the t-table = 2.02. Conclusion: Educational video is effective in improving oral hygiene in preschool students by reducing the plaque accumulation.

Food image classification and image retrieval based on visual features and machine learning

Research on image retrieval and classification in the food field has become one of the more and more concerned research topics in the field of multimedia analysis and applications. In recent years, with the rapid development of the Internet industry and multimedia technology, image classification and retrieval technology has become a research hotspot at home and abroad. Traditional keyword-based image retrieval and image classification have been unable to meet people’s daily needs; so, image recognition methods based on image content came into being. The most representative of image feature description methods are mainly two aspects: image visual features and image abstract semantics extracted based on machine learning algorithms. These two algorithms have their own key points in describing images, which are difficult to achieve the desired results in image classification and image retrieval. Based on this, this paper proposes research on food image classification and image retrieval methods based on visual features and machine learning. This paper proposes a food image retrieval and classification method based on Faster R-CNN network. This paper selects food image sets from the visual gene database to fine-tune the Faster R-CNN network to ensure the accuracy of Faster R-CNN food area detection, and experimented on the Dish-233 food dataset, which is a subset of the dish dataset, including 233 dishes and 49,168 images. The experimental results in this paper show that the performance of this method is better than other methods in terms of image classification performance. Compared with CNN-GF, the performance is improved by 5%. In terms of image retrieval, this method also shows its superiority This proves that compared with other methods, the proposed method has more discriminative visual features, and its performance has been improved in food image retrieval and classification tasks.

Cloud-Based Multimedia Content Protection System

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.

Developing Asertive Media Based On Android

This work aimed to develop asertif based on android. Research setting are at Junior High School 3 Godean. Media developed by combine any multimedia object, including text, images, audio, video, and interactive buttons. Content of media such as video to gain understanding about asertif behavior, 15 questions, and social media researcher information. Research method using research and development and alessi and trolip procedures. Research procedure including (1) research plan, (2) design, including flowcart and story board development, (3) development, including media production using code program, alpha, revise, and beta tested. Percentage tested found that content tested 75% (worthy), media tested 81,5% (very worthy). Student tested result 83,6% (very worthy).

Role of Digital Image Processing in Image Art under the Background of Big Data

With the development of society, digital image, the product of the development of digital technology, is more and more used in multimedia image art. With the rapid development of science and technology, in the 21st century, the use of digital image in art works has been very popular, showing a blowout trend. Digital technology is integrated into image art in a way of mutual integration. How to make digital image play a very good auxiliary role in art works? It makes the finishing point, but it doesn’t make the works dazzle and lose one thing or another. The purpose of this paper is to explore the role of digital image processing in the image art under the background of big data, so as to combine the Internet of things application technology and update the existing mode of art performance. In this paper, we will use the research method of specific analysis to compare the data and come to a conclusion. The research results show that image communication plays an important role in promoting the spread of economy, culture and daily life. In the process of image communication, creativity is essential. In the current industrial development, creative industry, as a new industrial form, can organically combine with other industries, and effectively promote the rise and development of other industries.

DNA Computing Based Encryption Algorithm for Wireless Multimedia Communication System

Transmission of digital images for wireless multimedia communication system requires reliable security in storage which is a challenging task. So many works are carried out to develop and implement the cryptographic techniques for a wireless multimedia communication system. A traditional symmetric key cryptographic algorithm such as Light weight Data Encryption Standard cipher, Advanced Encryption Standard cipher, Triple-DES, Twofish, Blowfish, RC5 etc played a major role to transmit and receive the multimedia (text, image and video) data. Proposed DNA computing based authentication algorithm provides secure transmission of multimedia data by using symmetric key cryptography techniques. DNA computing techniques provides a higher level of security and enables the user to store a large amount of data. Finally, it shows the implemented result analysis the performance of different symmetric key cryptographic techniques using the simulation parameters such as power consumption, key size, processing time, memory space, speed and latency. It also improves the security level by incorporating the advantage of DNA cryptography to achieve a high level of security against various attacks.

Hiding shares by multimedia image steganography for optimized counting-based secret sharing

The secret sharing scheme is a data security tool that provides reliability and robustness for multi-user authentication systems. This work focus on improving the counting-based secret sharing technique for higher shares security as well as simple and fast computation. The research considers resolving some originally published defects in the shares reconstruction phase by proposing a new distribution model. This distribution model has been optimized practical and efficient. Also, the shares reconstruction model reflects increasing the security of the system by shares authenticity via steganography. We have employed multimedia image-based steganography methods to store the optimized shares that is presenting comparisons for proofed remarks. The paper experimentations tests the work of the enhancements by assuming different secret sharing key sizes of 64-bit, 128-bit, and 256-bit to make sure of practical variations within the security study. The shares usability has been further optimized by testing embedding each generated share using five different techniques of image-based steganography. The results showed a significant attractive impact making the optimized counting-based secret sharing scheme considered a promising solution for multi-user authentication security applications. This optimized system has been analyzed according to the distortion security and capacity parameters showing attractive contributions opening the research direction for further research to come.

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

With the rapid development of computer networks and multimedia technologies, images, which are important carriers of information dissemination, have made human cognition of things easier. Image recognition is a basic research task in computer vision, multimedia search, image understanding and other fields. This paper proposes a hierarchical feature learning structure that is completely automatically based on the original pixels of the image, and uses the K-SVD (K-Singular Value Decomposition) algorithm with label consistency constraints to train the discriminant dictionary. For different types of image data sets, the algorithm only extracts image blocks. After dense sampling, an efficient OMP (Orthogonal Matching Pursuit) encoder is used to obtain a layered sparse representation. The improved SIFT (Scale Invariant Feature Transform) algorithm is used to solve the difficult problem of multimedia visual image stereo matching. The feature point extraction and stereo matching of multimedia visual images, different scales and different viewpoint images are analyzed separately. Aiming at a large number of low-dimensional geometric features of 3D images, this paper studies the extraction and sorting strategies of low-dimensional geometric features of 3D images. A sparse representation method for 3D images is proposed, and the sparseness of image features is evaluated. This further improves the accuracy of 3D image representation and the robustness of 3D image recognition algorithms.

An efficient image system-based grey wolf optimiser method for multimedia image security using reduced entropy-based 3D chaotic map

Chaotic maps play an important role in information sharing. In this paper a grey wolf optimiser used with reduced entropy-based 3D chaotic map. The selection and high coefficients are selected based on the reduced entropy value to identify the optimised parameters to get unpredictable random values. Time complexity, autocorrelation of V, H and D elements, histogram of original and cipher images, peak signal to noise ratio and NPCR and UACI values are computed from the cipher image. The empirical results show the proposed method provides good, better imperceptibility and defends various attacks. To prove this accomplishment of the method, several experiments were conducted and compared the results with existing systems.

Multimedia Image Compression Method Based on Biorthogonal Wavelet and Edge Intelligent Analysis

At present, network image communication is still restricted by channel coding, image and multimedia transmission and other key technologies. Therefore, the transmission process needs to convert the image signal into a digital signal, and then use the relevant band compression technology to reduce these signals to narrow the occupied frequency band, that is, to reduce the amount of information to be transmitted in synchronous transmission. Wavelet transform is a powerful tool for image compression because of its low entropy, multi-resolution, decorrelation and flexible base selection. In this paper, a method of multimedia image compression based on biorthogonal wavelet packet is proposed, which includes the establishment of linear phase biorthogonal wavelet basis, the selection of 3 or 4 levels of wavelet decomposition and reconstruction stage, and the combination of improved band division and preservation strategy. Finally, a compression test is performed based on the selected wavelet basis function and the optimal decomposition and reconstruction layers of the standard test image, which enables to obtain a more ideal compression ratio.

An efficient image system-based grey wolf optimiser method for multimedia image security using reduced entropy-based 3D chaotic map

An efficient image system-based grey wolf optimiser method for multimedia image security using reduced entropy-based 3D chaotic map

Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

The authors propose WNAHVF, a combined weighted and normalized AlexNet with handcrafted visual features for extracting features from images and using those vectors for image retrieval and classification. The authors test the WNAHVF method on two general datasets, Corel-1k and Corel-10k, and one medical dataset. The outcomes demonstrate combining Bag of Features and Local Neighbor patterns with AlexNet enhances the accuracy and gives better results in general and medical image datasets in retrieval and classification problems. This algorithm gives results that are superior to existing strategies.

Development of the method of multi-level segmentation of images

As the description of the multilevel segmentation algorithm implies, the following operations are required for its implementation. This is to determine the brightness of the pixels, their average value, and their differences. In addition, a comparison operation is required: greater, less, equal to the difference of brightness with a fixed threshold value and the operation of masking (shift) in the case of image processing by scanning it with a standard mask image. Then a summation operation is required to calculate the number of positive, negative and null drugs and the operation of calculating the maximum of their product. As is evident from the above operations, the laborious and slow computational operations inherent in, for example, orthogonal transformations are not used to implement the proposed methods. This makes it promising to apply this approach to real-time multimedia image processing, followed by their classification.

Affective Computing for Large-scale Heterogeneous Multimedia Data

The wide popularity of digital photography and social networks has generated a rapidly growing volume of multimedia data (i.e., images, music, and videos), resulting in a great demand for managing, retrieving, and understanding these data. Affective computing (AC) of these data can help to understand human behaviors and enable wide applications. In this article, we survey the state-of-the-art AC technologies comprehensively for large-scale heterogeneous multimedia data. We begin this survey by introducing the typical emotion representation models from psychology that are widely employed in AC. We briefly describe the available datasets for evaluating AC algorithms. We then summarize and compare the representative methods on AC of different multimedia types, i.e., images, music, videos, and multimodal data, with the focus on both handcrafted features-based methods and deep learning methods. Finally, we discuss some challenges and future directions for multimedia affective computing.