Sharif Digital Repository

Classification of video events based on frame-level descriptors is a common approach to video recognition. In the meanwhile, proper encoding of the frame-level descriptors is vital to the whole event classification procedure. While there are some pretty efficient video descriptor encoding methods, temporal ordering of the descriptors is often ignored in these encoding algorithms. In this paper, we show that by taking into account the temporal inter-frame dependencies and tracking the chronological order of video sub-events, accuracy of event recognition is further improved. First, the frame-level descriptors are extracted using convolutional neural networks (CNNs) pre-trained on ImageNet,... 

This study introduces a new scheme for steganalysis of the least significant bit (LSB) embedding, based on the idea of closure of sets (CoS), which is independent of the type of cover signal, applicable to both spatial and transform domains. The CoS is referred to as some special subsets that could be found in a common space whose elements relate to higher-order statistical properties of the signal. The proposed scheme is used for steganalysis of the LSB steganography of greyscale TIFF and JPEG images and audio signals, employing a set of accurate and monotone features that are extracted based on the CoS definition. It is shown that significant improvement to the detection accuracy in... 

This paper presents a new scheme for steganalysis of random LSB embedding, capable of applying to any kind of digital signal in both spatial and transform domains. The proposed scheme is based on defining a space whose elements relate to higher-order statistical properties of the signal and looking for special subsets, which we call Closure of Sets (CoS) in this space. We use this scheme for steganalysis of the LSB steganography in grayscale images, employing a vector of five accurate and monotone features. Experimental results show significantly higher accuracy of the proposed scheme, as compared to those reported in the literature, especially in low embedding rates applications. © 2009... 

A robust fingerprint recognition system based on marginal statistics of 2D wavelet transform is introduced which significantly improves the accuracy of previous wavelet based approaches due to 1) a better selection of features extracted from the wavelet transform, and 2) a more accurate distance measure to find the similarity between fingerprints. A combination of Jain and Poincare algorithms is employed to locate the fingerprint reference point. The main part of the fingerprint image is chosen as a rectangle with the reference point at its center. The image is then divided into nonoverlapping sub-images, the wavelet transform is applied to the bi-level sub-images, and Generalized Gaussian... 

In this paper, Kullback-Leibler Distance (KLD) is employed to measure the dissimilarity between marginal statistical features of contourlet transform to fingerprint identification. Conourlet transform is a non separable two dimensional transform which can well capture the geometry of edges in the images which convey important information for the human visual system (HVS). Here, marginal statistics of each transform subband are modeled by a Generalized Gaussian Density (GGD) model and the GGD parameters-α and β- are granted as the extracted features from the corresponding subbands and the fingerprint recognition is done based on k-NN classifier employing Kullback-Leibler Distance (KLD)... 

This paper presents an event based scheme for uncompressed video watermarking. The video signal is assumed to be a sequence of overlapping visual components - called events. We address this overlapping structure of video contents and present an event based approach through employing a block based Temporal Decomposition (TD) scheme. The TD describes a set of spectral parameters of the video as a linear combination of a set of temporally overlapping compact event functions. We have applied the decomposition results to digital video watermarking. To construct the matrix of parameters in the TD, Multiresolution Singular Value Decomposition (MR-SVD) is utilized and singular values of a set of... 

This paper addresses the feasibility of applying realtime steganalysis to the Internet data. To reach a practical solution, we have employed offline procedures to minimize the volume of the data to be processed by an online real-time system. Possible offline services that can be provided to an online system then led us to appropriate network designs for the online system. To go into detail of the online systems, we have carefully analyzed some well-known, state-of-the-art steganalysis algorithms and estimated their processing power and memory requirements. Some formulas are derived to relate the network bit rate with the number of the processing units, their processing power, and memory... 

We propose a novel image steganalysis method, based on singular value decomposition and noise estimation, for the spatial domain LSB embedding families. We first define a content independence parameter, DS, that is calculated for each LSB embedding rate. Next, we estimate the DS curve and use noise estimation to improve the curve approximation accuracy. It is shown that the proposed approach gives an estimate of the LSB embedding rate, as well as information about the existence of the embedded message (if any). The proposed method can effectively be applied to a wide range of the image LSB steganography families in spatial domain. To evaluate the proposed scheme, we applied the method to a... 

We present an image steganographic technique to preserve the first-order and the second-order statistics of the host image. Experimental results show that the proposed method can preserve the image quality and also resists typical statistical analysis based attacks. The new steganography method solves the distortion problem with the HKC algorithm and hence thwarts the Kuo attack. The method is based on the idea of histogram shift, in which two empty bins are produced in the image histogram that are then filled up using their neighboring bins through the embedding process. © 2009 IEEE  

This paper introduces an efficient scheme to image steganography by introducing the hidden message (payload) insertion in spatial domain and transforming the stego-image to compressed domain. We apply a recently-proposed LSB method in order to obtain better statistical behavior of the stego-message and subsequently, the obtained stego-image is transformed and quantized in order to enhance the security of hiding. Performance analysis comparisons confirm a higher efficiency for our proposed method. Compared to recently-proposed approaches, our method offers the advantage that it combines an efficient LSB method with transform domain security. © 2009 IEEE  

Achieving high rates of detection in low rates of embedding is still a challenging problem in many steganalysis systems. The newly proposed steganalysis system based on sparse representation classifier has shown remarkable detection rates in low embedding rate. In this paper, we propose a new steganalysis system based on double sparse representation classifier. We compare our proposed method with other steganalysis systems which use different classifier (including nearest neighbor, support vector machine, ensemble support vector machine and sparse representation). In all of our experiments, input features to the classifier are fixed and the ability of classifier is examined. Also we provide... 

Achieving high rates of detection in low rates of embedding is still a challenging problem in many steganalysis systems. The newly proposed steganalysis system based on sparse representation classifier has shown remarkable detection rates in low embedding rate. In this paper, we propose a new steganalysis system based on double sparse representation classifier. We compare our proposed method with other steganalysis systems which use different classifier (including nearest neighbor, support vector machine, ensemble support vector machine and sparse representation). In all of our experiments, input features to the classifier are fixed and the ability of classifier is examined. Also we provide... 

A major class of image and video watermarking algorithms, i.e. content-based watermarking, is based on the concept of Human Visual System (HVS) in order to adapt more efficiently to the local characteristics of the host signal. In this paper, a content-based video watermarking scheme is developed and the concept of entropy masking effect is employed to significantly improve the use of the HVS model. Entropy masking effect states that the human eye's sensitivity decreases in high entropy regions, i.e. regions with spatial or temporal complexity. The spatial entropy masking effect has been exploited in a number of previous works in order to enhance the robustness of image-adaptive watermarks.... 

A new method for temporal decomposition (TD) of speech parameters for very low rate coding applications is developed. Unlike typical TD, the phonetic relevance is not considered here, instead, we represent the spectral parameters of speech using pre-defined interpolation functions. These functions are located at instants, which give maximum correlation with the true event structure. In this method, no event refinement is required, which significantly reduces the computational complexity of the coder to make real-time implementation possible. The method is also highly flexible and can comply with diverse coding system attributes such as bit-rate, accuracy, delay, and complexity. A spectral... 

In this paper, a novel multi-head multi-layer perceptron (MLP) structure is presented for implicit neural representation (INR). Since conventional rectified linear unit (ReLU) networks are shown to exhibit spectral bias towards learning low-frequency features of the signal, we aim at mitigating this defect by taking advantage of local structure of the signals. To be more specific, an MLP is used to capture the global features of the underlying generator function of the desired signal. Then, several heads are utilized to reconstruct disjoint local features of the signal, and to reduce the computational complexity, sparse layers are deployed for attaching heads to the body. Through various... 

This letter proposes a novel normalized subband adaptive filter (NSAF) algorithm, which applies variable step sizes to subband filters to improve the convergence performance of the conventional NSAF and update only a subset of the subbands per iteration to reduce its computational complexity. The selection process for each subband is based on the amount of improvement it makes to the mean square deviation at every iteration. Simulation results show significant reduction in computational complexity, faster convergence rate, and lower misadjustment error achieved using the proposed scheme  

In this paper, a robust multiplicative video watermarking scheme is presented. We segment the video signal into 3-D blocks like cubes, and then apply 3-D wavelet transform to each block. The watermark is inserted through multiplying the low frequency wavelet coefficients by a constant parameter that controls the power of the watermark. The proposed watermark extraction procedure is based on the maximum likelihood rule applied to the watermarked wavelet coefficients  

A new steganalysis system for JPG-based image data hiding is proposed in this paper. We use features extracted from both wavelet and DCT domains that are refined later in the sense of utmost discrimination between the clear and stego images in the classification system. Statistical properties of the SVD of wavelet sub-bands are combined with the extended DCT-Markov features, and the features that are most sensitive to the data embedding are chosen through a SVM-RFE based selection algorithm. Experimental results show significant improvement over baseline methods, especially for steganalysis of Perturbed Quantization (PQ), which is known to be one of most secure JPG-based steganography... 

Niosomes are vesicles formed mostly by nonionic surfactant and cholesterol incorporation as an excipient. The drug entrapment efficiency of niosomal vesicles is particularly important and depends on many parameters. Changing the effective parameters to have maximum entrapment efficiency in the laboratory is time-consuming and costly. In this study, a machine learning framework was proposed to address these problems. In order to find the most critical parameter affecting the entrapment efficiency and its optimal value in a specific experiment, data were first extracted from articles of the last decade using keywords of niosome and thin-film hydration method. Then, deep neural network (DNN),... 

Deep learning architectures employ various regularization terms to handle different types of priors. Non-smooth regularization terms have shown promising performance in the deep learning architectures and a learning framework has recently been proposed to train autoencoders with such regularization terms. While this framework efficiently manages the non-smooth term during training through proximal operators, it is limited to autoencoders and suffers from low convergence speed due to several optimization sub-problems that must be solved in a row. In this paper, we address these issues by extending the framework to general feed-forward neural networks and introducing variable extrapolation...