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In this paper, we present a new adaptive contourlet-based steganography method that hides secret data in a specific or automatically selected cover image. Our proposed steganography method primarily decomposes the cover image by contourlet transform. Then, every bit of secret data is embedded by increasing or decreasing the value of one coefficient in a block of a contourlet subband. Contourlet coefficients are manipulated relative to their magnitudes to hide the secret data adaptively. In addition to proposing contourlet-based steganography method, this work investigates the effect of cover selection on steganography embedding and steganalysis results. We demonstrate, through the... 

This paper presents a fuzzy approach for contrast enhancement, based on two multi-scale transforms, namely wavelet and contourlet transforms. Separability and nondirectionality of conventional 2D wavelet transform, makes it unsuitable for sparsely representation of curve or line shaped image objects. On the other hand, the contourlet transform is a good alternative for this purpose. In this paper, coefficient enhancement, both in wavelet and contourlet spaces, is carried out by making use of simple fuzzy rules. These rules make the enhancement procedure more understandable and flexible. With this method, the knowledge and experience of the expert from the distribution of the coefficients can... 

An ideal steganographic technique embeds secret information into a carrier cover object with virtually imperceptible modification of the cover object. Steganalysis is a technique to discover the presence of hidden embedded information in a given object. Each steganalysis method is composed of feature extraction and feature classification components. Using features that are more sensitive to information hiding yields higher success in steganalysis. So far, several steganalysis methods have been presented which extract some features from DCT or wavelet coefficients of images. Multi-scale and time-frequency localization of an image is offered by wavelets. However, wavelets are not effective in... 

In this paper, a new adaptive steganographic scheme based on contourlet transform is presented that provides large embedding capacity. In this method, embedding is done in contourlet transform domain. The contourlet coefficients with larger magnitude that correspond to the edges are selected for embedding. This selection is due to less sensitivity of human eyes to non-smooth regions. Each bit of secret data is embedded by exchanging the value of two coefficients in a 4×4 block of a contourlet subband. The proposed method is examined with two strong steganalysis algorithms and the results show that we could successfully embed data in a cover image with the capacity of 0.05 bits per pixel.... 

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)... 

Recently many works focus on the vehicle type recognition because it is important in security and authentication systems. Computational complexity and low recognition rate especially when the system has to recognize among a large number of vehicles, are two major problems in vehicle type recognition. In recent years wavelet and contourlet transform have been applied in the recognition tasks successfully. In this paper we proposed a method for recognizing vehicle type in different lighting conditions. We used wavelet and contourlet as tools for feature extraction. These features are powerful and robust to illumination and scale variation. We reduced the dimension of feature vector by resizing... 

We propose a new nonblind multiresolution watermarking method for still images based on the contourlet transform (CT). In our approach, the watermark is a grayscale image which is embedded into the highest frequency subband of the host image in its contourlet domain. We demonstrate that in comparison to other methods, this method enables us to embed more amounts of data into the directional subbands of the host image without degrading its perceptibility. The experimental results show robustness against several common watermarking attacks such as compression, adding noise, filtering, and geometrical transformations. Since the proposed approach can embed considerable payload, while providing... 

Steganalysis is a technique to detect the presence of hidden embedded information in a given data. Each steganalyzer is composed of feature extraction and feature classification components. Using features that are more sensitive to data hiding yields higher success in steganalysis. The present paper offers a new universal approach to steganalysis that uses statistical moments of contourlet coefficients as features for analysis. A non-linear SVM classifier is used to classify cover and stego images. The effectiveness of the proposed method is demonstrated by extensive experimental investigations. The proposed steganalysis method is compared with two well known steganalyzers against typical... 

The first step of any face processing system is detecting the location in images where faces are present. In this paper we present an upright frontal face detection system based on the multi-resolution analysis of the face. In this method firstly, skin-color information is used to detect skin pixels in color images; then, the skin-region blocks are decomposed into frequency sub-bands using contourlet transform. Features extracted from sub-bands are used to detect face in each block. A multi-layer perceptrone (MLP) neural network was trained to do this classification. To decrease false positive detection we use eyes and lips template matching. These templates achieved by averaging... 

In this paper, an improved multiplicative image watermarking system is presented. Since human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in directional subband with the highest energy. By modeling the contourlet coefficients with General Gaussian Distribution (GGD), the distribution of watermarked noisy coefficients is analytically calculated. The tradeoff between the transparency and robustness of the watermark data is solved in a novel fashion. At the receiver, based on the Maximum Likelihood (ML) decision rule, an optimal detector by the aid... 

A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The... 

In this paper, a new multiplicative image watermarking system is presented. As human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in the most energetic directional subband. By modeling General Gaussian Distribution (GGD) for the contourlet coefficients, the distribution of watermarked noisy coefficients is analytically calculated. At the receiver, based on the Maximum Likelihood (ML) decision rule, the optimal detector is proposed. Experimental results show the imperceptibility and high robustness of the proposed method against Additive White... 

Fusion of high spectral but low spatial resolution multispectral and low spectral but high spatial resolution panchromatic satellite images is a very useful technique in various remote sensing applications, such as change detection. Recently, some studies showed that wavelet-based image fusion methods provide high quality spectral content of the fused image. However, most of wavelet-based methods have a spatial resolution of the fused result less than the Brovey, IHS, and PCA fusion methods. This is mainly because real wavelet transform cannot efficiently represent the singularity of linear/curved contents. In this paper, we introduce a new method based on the contourlet transform which... 

We propose an effective blind image steganalysis based on contourlet transform and Zernike moments that improves the detection accuracy of universal image steganalysis methods. The proposed method examines randomness in the test image to distinguish between the stego and non-stego images. The suspicious image is decomposed by contourlet transform, and then the absolute Zernike moments of contourlet subbands coefficients of the image and linear prediction error of each contourlet subband are extracted as features for steganalysis. These features are fed to a nonlinear SVM classifier with an RBF kernel to distinguish between cover and stego images. Experimental results show that the proposed... 

In this paper, we propose a new multiresolution watermarking method for still images based on the Contourlet Transform (CT). In our algorithm, the watermark is a grayscale image which is embedded into the host image in its contourlet domain. We demonstrate that in comparison with other methods, the proposed method enables us to embed more amount of data into the host image without degrading its perceptual quality. The experimental results show robustness against several common watermarking attacks such as compression, adding noise, and filtering. ©2009 IEEE  

Detection of skin pixels in arbitrary images is addressed in this paper. We have combined texture and color information to segment skin regions. First, a pixel-based boosted skin detection method is used to locate skin pixels. To further improve the detect performance, skin region texture features are employed using the nonsubsampled contourlet coefficients. For the candidate skin pixels, the set of 8×8 patches around that pixel in all subimages are selected and the feature vector of each patch is extracted. Multilayer perceptron is then utilized to learn features and classify any given input sample. The proposed algorithm has achieved true positive rate of about 82.8% and false positive...