Biblio

Hierarchical approaches for representation learning have the ability to encode relevant features at multiple scales or levels of abstraction. However, most hierarchical approaches exploit only the last level in the hierarchy, or provide a multiscale representation that holds a significant amount of redundancy. We argue that removing redundancy across the multiple levels of abstraction is important for an efficient representation of compositionality in object-based representations. With the perspective of feature learning as a data compression operation, we propose a new greedy inference algorithm for hierarchical sparse coding. Convolutional matching pursuit with a L0-norm constraint was used to encode the input signal into compact and non-redundant codes distributed across levels of the hierarchy. Simple and complex synthetic datasets of temporal signals were created to evaluate the encoding efficiency and compare with the theoretical lower bounds on the information rate for those signals. Empirical evidence have shown that the algorithm is able to infer near-optimal codes for simple signals. However, it failed for complex signals with strong overlapping between objects. We explain the inefficiency of convolutional matching pursuit that occurred in such case. This brings new insights about the NP-hard optimization problem related to using L0-norm constraint in inferring optimally compact and distributed object-based representations.

Specifics of an alias-free digitizer application for compressed digitizing and recording of wideband signals are considered. Signal sampling in this case is performed on the basis of picosecond resolution event timing, the digitizer actually is a subsystem of Event Timer A033-ET and specific events that are detected and then timed are the signal and reference sine-wave crossings. The used approach to development of this subsystem is described and some results of experimental studies are given.

In this paper, we propose a new color image encryption and compression algorithm based on the DNA complementary rule and the Chinese remainder theorem, which combines the DNA complementary rule with quantum chaotic map. We use quantum chaotic map and DNA complementary rule to shuffle the color image and obtain the shuffled image, then Chinese remainder theorem from number theory is utilized to diffuse and compress the shuffled image simultaneously. The security analysis and experiment results show that the proposed encryption algorithm has large key space and good encryption result, it also can resist against common attacks.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

Compression is desirable for network applications as it saves bandwidth. Differently, when data is compressed before being encrypted, the amount of compression leaks information about the amount of redundancy in the plaintext. This side channel has led to the “Browser Reconnaissance and Exfiltration via Adaptive Compression of Hypertext (BREACH)” attack on web traffic protected by the TLS protocol. The general guidance to prevent this attack is to disable HTTP compression, preserving confidentiality but sacrificing bandwidth. As a more sophisticated countermeasure, fixed-dictionary compression was introduced in 2015 enabling compression while protecting high-value secrets, such as cookies, from attacks. The fixed-dictionary compression method is a cryptographically sound countermeasure against the BREACH attack, since it is proven secure in a suitable security model. In this project, we integrate the fixed-dictionary compression method as a countermeasure for BREACH attack, for real-world client-server setting. Further, we measure the performance of the fixed-dictionary compression algorithm against the DEFLATE compression algorithm. The results evident that, it is possible to save some amount of bandwidth, with reasonable compression/decompression time compared to DEFLATE operations. The countermeasure is easy to implement and deploy, hence, this would be a possible direction to mitigate the BREACH attack efficiently, rather than stripping off the HTTP compression entirely.

Integrity of image data plays an important role in data communication. Image data contain confidential information so it is very important to protect data from intruder. When data is transmitted through the network, there may be possibility that data may be get lost or damaged. Existing system does not provide all functionality for securing image during transmission. i.e image compression, encryption and user authentication. In this paper hybrid cryptosystem is proposed in which biometric fingerprint is used for key generation which is further useful for encryption purpose. Secret fragment visible mosaic image method is used for secure transmission of image. For reducing the size of image lossless compression technique is used which leads to the fast transmission of image data through transmission channel. The biometric fingerprint is useful for authentication purpose. Biometric method is more secure method of authentication because it requires physical presence of human being and it is untraceable.

This paper proposes a novel plan of compacting encoded pictures with helper data. The substance manager scrambles the first uncompressed pictures furthermore creates some helper data, which will be utilized for information pressure and picture recreation. At that point, the channel supplier who can't get to the first substance may pack the encoded information by a quantization technique with ideal parameters that are gotten from a piece of helper data and a pressure proportion mutilation criteria, and transmit the packed information, which incorporate a scrambled sub-picture, the quantized information, the quantization parameters and an alternate piece of assistant data. At recipient side, the key picture substance can be reproduced utilizing the packed scrambled information and the mystery key.

In our digital world internet is a widespread channel for transmission of information. Information that is transmitted can be in form of messages, images, audios and videos. Due to this escalating use of digital data exchange cryptography and network security has now become very important in modern digital communication network. Cryptography is a method of storing and transmitting data in a particular form so that only those for whom it is intended can read and process it. The term cryptography is most often associated with scrambling plaintext into ciphertext. This process is called as encryption. Today in industrial processes images are very frequently used, so it has become essential for us to protect the confidential image data from unauthorized access. In this paper Advanced Encryption Standard (AES) which is a symmetric algorithm is used for encryption and decryption of image. Performance of Advanced Encryption Standard algorithm is further enhanced by adding a key stream generator W7. NIOS II soft core processor is used for implementation of encryption and decryption algorithm. A system is designed with the help of SOPC (System on programmable chip) builder tool which is available in QUARTUS II (Version 10.1) environment using NIOS II soft core processor. Developed single core system is implemented using Altera DE2 FPGA board (Cyclone II EP2C35F672). Using MATLAB the image is read and then by using DWT (Discrete Wavelet Transform) the image is compressed. The image obtained after compression is now given as input to proposed AES encryption algorithm. The output of encryption algorithm is given as input to decryption algorithm in order to get back the original image. The implementation of which is done on the developed single core platform using NIOS II processor. Finally the output is analyzed in MATLAB by plotting histogram of original and encrypted image.

Security of secret data has been a major issue of concern from ancient time. Steganography and cryptography are the two techniques which are used to reduce the security threat. Cryptography is an art of converting secret message in other than human readable form. Steganography is an art of hiding the existence of secret message. These techniques are required to protect the data theft over rapidly growing network. To achieve this there is a need of such a system which is very less susceptible to human visual system. In this paper a new technique is going to be introducing for data transmission over an unsecure channel. In this paper secret data is compressed first using LZW algorithm before embedding it behind any cover media. Data is compressed to reduce its size. After compression data encryption is performed to increase the security. Encryption is performed with the help of a key which make it difficult to get the secret message even if the existence of the secret message is reveled. Now the edge of secret message is detected by using canny edge detector and then embedded secret data is stored there with the help of a hash function. Proposed technique is implemented in MATLAB and key strength of this project is its huge data hiding capacity and least distortion in Stego image. This technique is applied over various images and the results show least distortion in altered image.

This paper reports a research work on how to transmit a secured image data using Discrete Wavelet Transform (DWT) in combination of Advanced Encryption Standard (AES) with low power and high speed. This can have better quality secured image with reduced latency and improved throughput. A combined model of DWT and AES technique help in achieving higher compression ratio and simultaneously it provides high security while transmitting an image over the channels. The lifting scheme algorithm is realized using a single and serialized DT processor to compute up to 3-levels of decomposition for improving speed and security. An ASIC circuit is designed using RTL-GDSII to simulate proposed technique using 65 nm CMOS Technology. The ASIC circuit is implemented on an average area of about 0.76 mm2 and the power consumption is estimated in the range of 10.7-19.7 mW at a frequency of 333 MHz which is faster compared to other similar research work reported.

Mobile and aerial robots used in urban search and rescue (USAR) operations have shown the potential for allowing us to explore, survey and assess collapsed structures effectively at a safe distance. RGB-D cameras, such as the Microsoft Kinect, allow us to capture 3D depth data in addition to RGB images, providing a significantly richer user experience than flat video, which may provide improved situational awareness for first responders. However, the richer data comes at a higher cost in terms of data throughput and computing power requirements. In this paper we consider the problem of live streaming RGB-D data over wired and wireless communication channels, using low-power, embedded computing equipment. When assessing a disaster environment, a range camera is typically mounted on a ground or aerial robot along with the onboard computer system. Ground robots can use both wireless radio and tethers for communications, whereas aerial robots can only use wireless communication. We propose a hybrid lossless and lossy streaming compression format designed specifically for RGB-D data and investigate the feasibility and usefulness of live-streaming this data in disaster situations.
 

With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the mean time, social networking platform based on cloud computing has made multimedia big data sharing in social network easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social network. In this paper, we propose a scheme for implementing the Tree-Structured Harr (TSH) transform in a homomorphic encrypted domain for fingerprinting using social network analysis with the purpose of protecting media distribution in social networks. The motivation is to map hierarchical community structure of social network into tree structure of TSH transform for JPEG2000 coding, encryption and fingerprinting. Firstly, the fingerprint code is produced using social network analysis. Secondly, the encrypted content is decomposed by the TSH transform. Thirdly, the content is fingerprinted in the TSH transform domain. At last, the encrypted and fingerprinted contents are delivered to users via hybrid multicast-unicast. The use of fingerprinting along with encryption can provide a double-layer of protection to media sharing in social networks. Theory analysis and experimental results show the effectiveness of the proposed scheme.
 

The development of data communications enabling the exchange of information via mobile devices more easily. Security in the exchange of information on mobile devices is very important. One of the weaknesses in steganography is the capacity of data that can be inserted. With compression, the size of the data will be reduced. In this paper, designed a system application on the Android platform with the implementation of LSB steganography and cryptography using TEA to the security of a text message. The size of this text message may be reduced by performing lossless compression technique using LZW method. The advantages of this method is can provide double security and more messages to be inserted, so it is expected be a good way to exchange information data. The system is able to perform the compression process with an average ratio of 67.42 %. Modified TEA algorithm resulting average value of avalanche effect 53.8%. Average result PSNR of stego image 70.44 dB. As well as average MOS values is 4.8.

Today in the world of globalization mobile communication is one of the fastest growing medium though which one sender can interact with other in short time. During the transmission of data from sender to receiver, size of data is important, since more data takes more time. But one of the limitations of sending data through mobile devices is limited use of bandwidth and number of packets transmitted. Also the security of these data is important. Hence various protocols are implemented which not only provides security to the data but also utilizes bandwidth. Here we proposed an efficient technique of sending SMS text using combination of compression and encryption. The data to be send is first encrypted using Elliptic curve Cryptographic technique, but encryption increases the size of the text data, hence compression is applied to this encrypted data so the data gets compressed and is send in short time. The Compression technique implemented here is an efficient one since it includes an algorithm which compresses the text by 99.9%, hence a great amount of bandwidth gets saved.The hybrid technique of Compression-Encryption of SMS text message is implemented for Android Operating Systems.

The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

H.264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.