Biblio

In order to improve the status monitoring and management ability of wireless sensor networks, a dynamic data compression method based on grid deduplication is proposed. Grid-based sensor node spatial positioning and big data fusion method are adopted to realize dynamic feature mining of wireless sensor network data, extract feature sequence points of wireless sensor network data, reconstruct wireless sensor network data feature space by adopting spatial grid node recombination, build a statistical detection model of dynamic feature mining of wireless sensor network data by combining grid area grouping compression method, and realize embedded fuzzy control and joint feature distributed adaptive learning. The association matching degree of wireless sensor network data is analyzed. Combining fuzzy subspace compression and big data fusion clustering, the quantitative regression analysis model of wireless sensor network data is established. The time series reorganization of wireless sensor network database is realized by index table name, index column and other information. Compressed sensing method is used in linear fusion subspace to realize data compression and adaptive detection of wireless sensor network. Constraint feature points of wireless sensor network data compression are constructed, and dynamic compression and clustering processing of wireless sensor network data are realized at constraint points. Simulation results show that the feature clustering of data compression in wireless sensor networks is better and the storage space of data is reduced.

We propose an information-theoretical method of attribution of literary texts, developed within the framework of information theory and mathematical statistics. Using the proposed method, the following two problems of disputed authorship in Russian and Soviet literature were investigated: i) the problem of false attribution of some novels to Nekrasov and ii) the problem of dubious attribution of two novels to Bulgakov. The research has shown the high efficiency of the data-compression method for attribution of literary texts.

Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.

Image watermarking is very important phenomenon in modern society where intellectual property right of information is necessary. Considering this impending problem, there are many image watermarking methods exist in the literature each of having some key advantages and disadvantages. After summarising state-of-the-art literature survey, an optimum digital watermark technique using singular value decomposition with principle component analysis (PCA) is proposed and verified. Basically, the host image is compressed using PCA which reduces multi-dimensional data to effective low-dimensional information. In this scheme, the watermark is embedded using the discrete wavelet transformation-singular value decomposition approach. Simulation results show that the proposed method improves the system performance compared with the existing method in terms of the watermark embedding, and extraction time. Therefore, this work is valuable for image watermarking in modern life such as tracing copyright infringements and banknote authentication.

Universal style transfer methods typically leverage rich representations from deep Convolutional Neural Network (CNN) models (e.g., VGG-19) pre-trained on large collections of images. Despite the effectiveness, its application is heavily constrained by the large model size to handle ultra-resolution images given limited memory. In this work, we present a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the convolutional filters. The main idea is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for style transfer models. Moreover, to overcome the feature size mismatch when applying collaborative distillation, a linear embedding loss is introduced to drive the student network to learn a linear embedding of the teacher's features. Extensive experiments show the effectiveness of our method when applied to different universal style transfer approaches (WCT and AdaIN), even if the model size is reduced by 15.5 times. Especially, on WCT with the compressed models, we achieve ultra-resolution (over 40 megapixels) universal style transfer on a 12GB GPU for the first time. Further experiments on optimization-based stylization scheme show the generality of our algorithm on different stylization paradigms. Our code and trained models are available at https://github.com/mingsun-tse/collaborative-distillation.

One of the significant advancements in information technology is Cloud computing, but the security issue of data storage is a big problem in the cloud environment. That is why a system is proposed in this paper for improving the security of cloud data using encryption, information concealment, and hashing functions. In the data encryption phase, we implemented hybrid encryption using the algorithm of AES symmetric encryption and the algorithm of RSA asymmetric encryption. Next, the encrypted data will be hidden in an image using LSB algorithm. In the data validation phase, we use the SHA hashing algorithm. Also, in our suggestion, we compress the data using the LZW algorithm before hiding it in the image. Thus, it allows hiding as much data as possible. By using information concealment technology and mixed encryption, we can achieve strong data security. In this paper, PSNR and SSIM values were calculated in addition to the graph to evaluate the image masking performance before and after applying the compression process. The results showed that PSNR values of stego-image are better for compressed data compared to data before compression.

With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenarios and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.

It is challenging to detect weak hidden information in a JPEG compressed image. In this paper, we propose a 32-layer convolutional neural networks (CNNs) with feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient and information, and the shared features and bottleneck layers in the proposed CNN model further reduce the number of parameters dramatically. The experimental results shown that the proposed method significantly reduce the detection error rate compared with the existing JPEG steganalysis methods, e.g. state-of-the-art XuNet method and the conventional SCA-GFR method. Compared with XuNet method and conventional method SCA-GFR in detecting J-UNIWARD at 0.1 bpnzAC (bit per non-zero AC DCT coefficient), the proposed method can reduce detection error rate by 4.33% and 6.55% respectively.

Information security has developed rapidly over the recent years with a key being the emergence of social media. To standardize this discipline, security of an individual becomes an urgent concern. In 2019, it is estimated that there will be over 2.5 billion social media users around the globe. Unfortunately, anonymous identity has become a major concern for the security advisors. Due to the technological advancements, the phishers are able to access the confidential information. To resolve these issues numerous solutions have been proposed, such as biometric identification, facial and audio recognition etc prior access to any highly secure forum on the web. Generating digital signatures is the recent trend being incorporated in the field of digital security. We have designed an algorithm that after generating 68 point facial landmark, converts the image to a highly compressed and secure digital signature. The proposed algorithm generates a unique signature for an individual which when stored in the user account information database will limit the creation of fake or multiple accounts. At the same time the algorithm reduces the database storage overhead as it stores the facial identity of an individual in the form of a compressed textual signature rather than the traditional method where the image file was being stored, occupying lesser amount of space and making it more efficient in terms of searching, fetching and manipulation. A unique new analysis of the features produced at intermediate layers has been applied. Here, we opt to use the normal and two opposites' angular measures of the triangle as the invariance. It simply acts as the real-time optimized encryption procedure to achieve the reliable security goals explained in detail in the later sections.

Biometric system security requires cryptographic protection of sample data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed iris data by conducting iris recognition on the selectively encrypted data. This paper specifically compares the effects of a recently proposed approach, i.e. applying selective encryption to normalised texture data, to encrypting classical sample data. We assess achieved protection level as well as computational cost of the considered schemes, and particularly highlight the role of segmentation in obtaining surprising results.

Based on the analysis of existing wireless sensor networks(WSNs) security vulnerability, combining the characteristics of high encryption efficiency of the symmetric encryption algorithm and high encryption intensity of asymmetric encryption algorithm, a hybrid encryption algorithm based on wireless sensor networks is proposed. Firstly, by grouping plaintext messages, this algorithm uses advanced encryption standard (AES) of symmetric encryption algorithm and elliptic curve encryption (ECC) of asymmetric encryption algorithm to encrypt plaintext blocks, then uses data compression technology to get cipher blocks, and finally connects MAC address and AES key encrypted by ECC to form a complete ciphertext message. Through the description and implementation of the algorithm, the results show that the algorithm can reduce the encryption time, decryption time and total running time complexity without losing security.

A digital watermarking technique is an alternative method to protect the intellectual property of digital images. This paper presents a hybrid blind watermarking technique formulated by combining RDWT with SVD considering a trade-off between imperceptibility and robustness. Watermark embedding locations are determined using a modified entropy of the host image. Watermark embedding is employed by examining the orthogonal matrix U obtained from the hybrid scheme RDWT-SVD. In the proposed scheme, the watermark image in binary format is scrambled by Arnold chaotic map to provide extra security. Our scheme is tested under different types of signal processing and geometrical attacks. The test results demonstrate that the proposed scheme provides higher robustness and less distortion than other existing schemes in withstanding JPEG2000 compression, cropping, scaling and other noises.

This paper presents a scheme of intellectual property protection of hardware circuit based on digital compression coding technology. The aim is to solve the problem of high embedding cost and low resource utilization of IP watermarking. In this scheme, the watermark information is preprocessed by dynamic compression coding around the idle circuit of FPGA, and the free resources of the surrounding circuit are optimized that the IP watermark can get the best compression coding model while the extraction and detection of IP core watermark by activating the decoding function. The experimental results show that this method not only expands the capacity of watermark information, but also reduces the cost of watermark and improves the security and robustness of watermark algorithm.

The Data Compression is a creative skill which defined scientific concepts of providing contents in a compact form. Thus, it has turned into a need in the field of communication as well as in different scientific studies. Data transmission must be sufficiently secure to be utilized in a channel medium with no misfortune; and altering of information. Encryption is the way toward scrambling an information with the goal that just the known receiver can peruse or see it. Encryption can give methods for anchoring data. Along these lines, the two strategies are the two crucial advances that required for the protected transmission of huge measure of information. In typical cases, the compacted information is encoded and transmitted. In any case, this sequential technique is time consumption and computationally cost. In the present paper, an examination on simultaneous compression and encryption technique depends on DNA which is proposed for various sorts of secret data. In simultaneous technique, both techniques can be done at single step which lessens the time for the whole task. The present work is consisting of two phases. First phase, encodes the plaintext by 6-bits instead of 8-bits, means each character represented by three DNA nucleotides whereas to encode any pixel of image by four DNA nucleotides. This phase can compress the plaintext by 25% of the original text. Second phase, compression and encryption has been done at the same time. Both types of data have been compressed by their half size as well as encrypted the generated symmetric key. Thus, this technique is more secure against intruders. Experimental results show a better performance of the proposed scheme compared with standard compression techniques.

In this research project, we are interested by finding solutions to the problem of image analysis and processing in the encrypted domain. For security reasons, more and more digital data are transferred or stored in the encrypted domain. However, during the transmission or the archiving of encrypted images, it is often necessary to analyze or process them, without knowing the original content or the secret key used during the encryption phase. We propose to work on this problem, by associating theoretical aspects with numerous applications. Our main contributions concern: data hiding in encrypted images, correction of noisy encrypted images, recompression of crypto-compressed images and secret image sharing.

Model compression is considered to be an effective way to reduce the implementation cost of deep neural networks (DNNs) while maintaining the inference accuracy. Many recent studies have developed efficient model compression algorithms and implementations in accelerators on various devices. Protecting integrity of DNN inference against fault attacks is important for diverse deep learning enabled applications. However, there has been little research investigating the fault resilience of DNNs and the impact of model compression on fault tolerance. In this work, we consider faults on different data types and develop a simulation framework for understanding the fault resiliency of compressed DNN models as compared to uncompressed models. We perform our experiments on two common DNNs, LeNet-5 and VGG16, and evaluate their fault resiliency with different types of compression. The results show that binary quantization can effectively increase the fault resilience of DNN models by 10000x for both LeNet5 and VGG16. Finally, we propose software and hardware mitigation techniques to increase the fault resiliency of DNN models.

This paper proposes two adaptive Huffman coding hash tree algorithms to construct the hash tree of a file system. The algorithms are used to design the real-time proof of violation (PoV) scheme for the cloud storage service to achieve mutual non-repudiation between the user and the service provider. The PoV scheme can then generate cryptographic proofs once the service-level agreement (SLA) is violated. Based on adaptive Huffman coding, the proposed algorithms add hash tree nodes dynamically when a file is accessed for the first time. Every node keeps a count to reflect the frequency of occurrence of the associated file, and all nodes' counts and the tree structure are adjusted on-the-fly for every file access. This can significantly reduce the memory and computation overheads required by the PoV scheme. The file access patterns of the NCUCCWiki and the SNIA IOTTA datasets are used to evaluate the performance of the proposed algorithms. The algorithms are also compared with a related hash tree construction algorithm used in a PoV scheme, named ERA, to show their superiority in performance.

Methods for implementing integer arithmetic operations of addition, subtraction, and multiplication in the system of residual classes are considered. It is shown that their practical use in computer systems can significantly improve the performance of the implementation of arithmetic operations. A new method has been developed for raising numbers represented in the system of residual classes to an arbitrary power of a natural number, both in positive and in negative number ranges. An example of the implementation of the proposed method for the construction of numbers represented in the system of residual classes for the value of degree k = 2 is given.

A 2D-Compressive Sensing and hyper-chaos based image compression-encryption algorithm is proposed. The 2D image is compressively sampled and encrypted using two measurement matrices. A chaos based measurement matrix construction is employed. The construction of the measurement matrix is controlled by the initial and control parameters of the chaotic system, which are used as the secret key for encryption. The linear measurements of the sparse coefficients of the image are then subjected to a hyper-chaos based diffusion which results in the cipher image. Numerical simulation and security analysis are performed to verify the validity and reliability of the proposed algorithm.

With the increase of mobile equipment and transmission data, Common Public Radio Interface (CPRI) between Building Base band Unit (BBU) and Remote Radio Unit (RRU) suffers amounts of increasing transmission data. It is essential to compress the data in CPRI if more data should be transferred without congestion under the premise of restriction of fiber consumption. A data compression scheme based on Discrete Sine Transform (DST) and Lloyd-Max quantization is proposed in distributed Base Station (BS) architecture. The time-domain samples are transformed by DST according to the characteristics of Orthogonal Frequency Division Multiplexing (OFDM) baseband signals, and then the coefficients after transformation are quantified by the Lloyd-Max quantizer. The simulation results show that the proposed scheme can work at various Compression Ratios (CRs) while the values of Error Vector Magnitude (EVM) are better than the limits in 3GPP.

The main objective of this research work is to enhance the data storage capacity of the QR codes. By achieving the research aim, we can visualize rapid increase in application domains of QR Codes, mostly for smart cities where one needs to store bulk amount of data. Nowadays India is experiencing demonetization step taken by Prime Minister of the country and QR codes can play major role for this step. They are also helpful for cashless society as many vendors have registered themselves with different e-wallet companies like paytm, freecharge etc. These e-wallet companies have installed QR codes at cash counter of such vendors. Any time when a customer wants to pay his bills, he only needs to scan that particular QR code. Afterwards the QR code decoder application start working by taking necessary action like opening payment gateway etc. So, objective of this research study focuses on solving this issue by applying proposed methodology.

With the rapid development of computer networks, multimedia information is widely used, and the security of digital media has drawn much attention. The revised photo as a forensic evidence will distort the truth of the case badly tampered pictures on the social network can have a negative impact on the parties as well. In order to ensure the authenticity and integrity of digital media, self-recovery of digital images based on information hiding is studied in this paper. Jarvis half-tone change is used to compress the digital image and obtain the backup data, and then spread the backup data to generate the reference data. Hash algorithm aims at generating hash data by calling reference data and original data. Reference data and hash data together as a digital watermark scattered embedded in the digital image of the low-effective bits. When the image is maliciously tampered with, the hash bit is used to detect and locate the tampered area, and the image self-recovery is performed by extracting the reference data hidden in the whole image. In this paper, a thorough rebuild quality assessment of self-healing images is performed and better performance than the traditional DCT(Discrete Cosine Transform)quantization truncation approach is achieved. Regardless of the quality of the tampered content, a reference authentication system designed according to the principles presented in this paper allows higher-quality reconstruction to recover the original image with good quality even when the large area of the image is tampered.

Organizations not only need to defend their IT systems against external cyber attackers, but also from malicious insiders, that is, agents who have infiltrated an organization or malicious members stealing information for their own profit. In particular, malicious insiders can leak a document by simply opening it and taking pictures of the document displayed on the computer screen with a digital camera. Using a digital camera allows a perpetrator to easily avoid a log trail that results from using traditional communication channels, such as sending the document via email. This makes it difficult to identify and prove the identity of the perpetrator. Even a policy prohibiting the use of any device containing a camera cannot eliminate this threat since tiny cameras can be hidden almost everywhere. To address this leakage vector, we propose a novel screen watermarking technique that embeds hidden information on computer screens displaying text documents. The watermark is imperceptible during regular use, but can be extracted from pictures of documents shown on the screen, which allows an organization to reconstruct the place and time of the data leak from recovered leaked pictures. Our approach takes advantage of the fact that the human eye is less sensitive to small luminance changes than digital cameras. We devise a symbol shape that is invisible to the human eye, but still robust to the image artifacts introduced when taking pictures. We complement this symbol shape with an error correction coding scheme that can handle very high bit error rates and retrieve watermarks from cropped and compressed pictures. We show in an experimental user study that our screen watermarks are not perceivable by humans and analyze the robustness of our watermarks against image modifications.

With the rapid and radical evolution of information and communication technology, energy consumption for wireless communication is growing at a staggering rate, especially for wireless multimedia communication. Recently, reducing energy consumption in wireless multimedia communication has attracted increasing attention. In this paper, we propose an energy-efficient wireless image transmission scheme based on adaptive block compressive sensing (ABCS) and SoftCast, which is called ABCS-SoftCast. In ABCS-SoftCast, the compression distortion and transmission distortion are considered in a joint manner, and the energy-distortion model is formulated for each image block. Then, the sampling rate (SR) and power allocation factors of each image block are optimized simultaneously. Comparing with conventional SoftCast scheme, experimental results demonstrate that the energy consumption can be greatly reduced even when the receiving image qualities are approximately the same.

The term steganography was used to conceal thesecret message into other media file. In this paper, a novel imagesteganography is proposed, based on adaptive neural networkswith recycling the Improved Absolute Moment Block TruncationCoding algorithm, and by employing the enhanced five edgedetection operators with an optimal target of the ANNS. Wepropose a new scheme of an image concealing using hybridadaptive neural networks based on I-AMBTC method by thehelp of two approaches, the relevant edge detection operators andimage compression methods. Despite that, many processes in ourscheme are used, but still the quality of concealed image lookinggood according to the HVS and PVD systems. The final simulationresults are discussed and compared with another related researchworks related to the image steganography system.