3424954
Steganalysis of YASS
A promising steganographic method-yet another steganography scheme (YASS)-was designed to resist blind steganalysis via embedding data in randomized locations. In addition to a concrete realization which is named the YASS algorithm in this paper, a few strategies were proposed to work with the YASS algorithm in order to enhance the data embedding rate and security. In this work, the YASS algorithm and these strategies, together referred to as YASS, have been analyzed from a warden's perspective. It is observed that the embedding locations chosen by YASS are not randomized enough and the YASS embedding scheme causes detectable artifacts. We present a steganalytic method to attack the YASS algorithm, which is facilitated by a specifically selected steganalytic observation domain (SO-domain), a term to define the domain from which steganalytic features are extracted. The proposed SO-domain is not exactly, but partially accesses, the domain where the YASS algorithm embeds data. Statistical features generated from the SO-domain have demonstrated high effectiveness in detecting the YASS algorithm and identifying some embedding parameters. In addition, we discuss how to defeat the above-mentioned strategies of YASS and demonstrate a countermeasure to a new case in which the randomness of the embedding locations is enhanced. The success of detecting YASS by the proposed method indicates a properly selected SO-domain is beneficial for steganalysis and confirms that the embedding locations are of great importance in designing a secure steganographic scheme.
3471440
Universal JPEG steganalysis based on microscopic and macroscopic calibration
In this paper, we present a new universal steganalysis scheme to effectively attack some recently proposed JPEG steganography. Different from the other steganalyzers, not only the magnitude but also the sign dependencies existed in intra-block and inter-block quantized DCT (discrete cosine transform) coefficients are exploited by the Markov empirical transition matrices. Moreover, a new microscopic and macroscopic calibration method is proposed to calibrate the local and global distribution of the quantized DCT coefficients of the test image, thus improve the detecting performance. Experimental results demonstrate that our proposed scheme outperforms some existing steganalyzers in attacking the advanced JPEG steganography such as F5, MB1 and Outguess.
3441918
Effect of Different Coding Patterns on Compressed Frequency Domain Based Universal JPEG Steganalysis
Current steganalytic schemes for JPEG steganography are in favor of extracting features in DCT (Discrete Cosine Transform) domain and/or spatial domain. A recently proposed compressed frequency domain based universal steganalytic algorithm [21] showed concern over the statistics of Huffman compressed stream. The authors claimed that it was very effective in detecting stego images embedded by JPEG steganographic tools, including JPHide, F5 and OutGuess. Even though only several bytes were embedded, the scheme was still able to work, thus demonstrating astonishing steganalysis capability. By carefully controlled studies on the factors which may have impact on the new steganalytic method, we find out the truly cause of the powerfulness of this "payload-independent" steganalyzer. Experimental results reveal that different coding patterns used in cover and stego images, rather than the Avalanche Criterion [24] explained by its authors, have led to great detection efficiency. When the same coding pattern is employed in both cover and stego images, the performance of the newly devised steganalyzer has greatly dropped. Implication from this paper is that we should ensure the difference between the cover and stego images is only caused by data embedding itself in steganography and steganalysis.
3475098
Steganalysis of LSB Greedy Embedding Algorithm for JPEG Images using Coefficient Symmetry
A recently developed LSB greedy embedding algorithm for JPEG images is capable of resisting the chi-square attack. By carefully studying the quantized DCT (discrete cosine transform) coefficients of the cover and stego images, we find that the embedding algorithm does not preserve the histogram of the DCT coefficients well. In this paper, we define a new chi-square statistic which is used to measure whether the image under scrutiny is like the cover or the stego. Our proposed steganalytic method is based on the symmetry property of the DCT coefficients in JPEG images. It can also be used in the scenario where the cover images are double JPEG compressed. The reliability of this specific steganalytic scheme depends on the embedding rate and it is influenced by the JPEG quality factor. Experimental results show that when the embedding rate exceeds half of the maximal embedding capacity, the steganographic algorithm is detectable with a very low false negative rate, whatever the quality factor is.
3451766
Attack LSB Matching Steganography by Counting Alteration Rate of the Number of Neighbourhood Gray Levels
In this paper, we propose a new method for attacking the LSB (least significant bit) matching based steganography. Different from the LSB substitution, the least two or more significant bit-planes of the cover image would be changed during the embedding in LSB matching steganography and thus the pairs of values do not exist in stego image. In our proposed method, we get an image by combining the least two significant bit-planes and divide it into 3x3 overlapped subimages. The subimages are grouped into four types, i.e. T 1,T 2, T 3 and T 4 according to the count of gray levels. Via embedding a random sequence by LSB matching and then computing the alteration rate of the number of elements in T 1, we find that normally the alteration rate is higher in cover image than in the corresponding stego image. This new finding is used as the discrimination rule in our method. Experimental results demonstrate that the proposed algorithm is efficient to detect the LSB matching stegonagraphy on uncompressed gray scale images.
3472016
Detecting doubly compressed JPEG images by using Mode Based First Digit Features
In this paper, we utilize the probabilities of the first digits of quantized DCT (discrete cosine transform) coefficients from individual AC (alternate current) modes to detect doubly compressed JPEG images. Our proposed features, named by mode based first digit features (MBFDF), have been shown to outperform all previous methods on discriminating doubly compressed JPEG images from singly compressed JPEG images. Furthermore, combining the MBFDF with a multi-class classification strategy can be exploited to identify the quality factor in the primary JPEG compression, thus successfully revealing the double JPEG compression history of a given JPEG image.
1802579
Analysis and Improvement of An Iris Identification Algorithm
Iris recognition using complex valued 2D Gabor filters received a great success in broad applications. Such method encoded the iris phase information which was extracted according to the complex valued 2D filters' response. In this paper, we theoretically analyzed the complex-valued 2D Gabor filters. Based on analysis, we concluded that the real part of complex-valued 2D Gabor filter would reduce the whole system performance because it is not perfectly band passed filter. An improved algorithm then proposed to extract the phase information of iris image. The new method has more advantages such as lower false rate, much less space of codes and shorter time of encoding and matching. In order to reduce the disturbance of eyelashes, eyelids, specular reflections, or other noise, a matching template is designed by dilating the positions of them. Experimental results show that the latter method has an encouraging performance
34344940
A Distributed Hebb Neural Network for Network Anomaly Detection
One of the most challenging problems in anomaly detection is to develop scalable algorithms which are capable of dealing with large audit data, network traffic data, or alter data. In this paper a distributed neural network based on Hebb rule is presented to improve the speed and scalability of inductive learning. The speed is improved by randomly splitting a large data set into disjoint subsets and each subset data is presented to an independent neural network, these networks can be trained in distributed and each one in parallel. The analysis of completeness and risk bounds of competitive Hebb learning proof that the distributed Hebb neural network can avoid the accuracy being degraded as compared to running a single algorithm with the entire data. The experiments are performed on the KDD'99 Data set, which is a standard intrusion detection benchmark. Comparisons with other approaches on the same benchmark demonstrate the effectiveness and applicability of the proposed method.
15163844
Towards a Coordination Infrastructure for Pervasive Computing Environment Based on Tuplespace and Semantic Web
Pervasive computing is an emerging computing paradigm, which is expected to be part of our everyday life in the foreseeable future. The coordination among heterogeneous devices, services, and software agents will be vital for ensuring the success of their interactions. Tuplespace (TS) offers a coordination infrastructure for communication. With pervasive computing environment which is characterized as an open and dynamic distributed system become more ubiquitous, autonomous, and complex, this can pose some limitations to TS. In this paper we introduce Semantic Tuplespaces (STS) based on a combination of Semantic Web technologies and tuplespace computing. It acts as a middleware to enable communication and coordination based on the principle of publish and retrieve of semantic information. We describe overview of the conceptual model and discuss the necessary extensions to the original tuplespace. An initial version of the STS prototype system has been implemented based on Jena and Pellet, a DL reasoner. Finally, through an elaborate use case, we show the usability and benefits of our approach.
35782406
License management scheme with anonymous trust for digital rights management
One of the major issues raised by digital rights management systems concerns the protection of the user's privacy and anonymous consumption of content. However, most existing license management schemes for DRM systems do not support the protection of user privacy. Moreover, some other schemes such as PrecePt can only bind the license with a specified device though they concern privacy protection. In this paper, we propose a license management scheme named LMSAT (License Management Scheme with Anonymous Trust) which provides a more powerful and flexible license acquisition and usage tracking scheme to allow the user access the contents anytime, anywhere, and on any compliant devices anonymously.
16922329
A New Transmit Scheme Combining Beamforming with Space-Time Block Coding
In this paper, a new transmit scheme combining beamforming (BF) with space-time block coding (STBC) is proposed for correlated fading channels. Based on maximizing the output mean signal-to-noise ratio (SNR) at the receiver, the transmitter BF weight vectors are first derived. Then, utilizing the minimal bit error rate (BER) upper bound as the design criterion, a simple power allocation algorithm is developed. Next, the BER performance of the system with the proposed transmit scheme is analyzed by considering an M-QAM constellation. Finally, computer simulation results are given to verify the effectiveness of the proposed scheme.