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Feature extraction is very important for steganalysis of content-adaptive JPEG steganography. The scale co-occurrence matrix feature based on a two-dimensional (2-D) Gabor filter is proposed, and diverse quantization for filter residuals is utilized to improve the detection performance. First, the definition of scale co-occurrence matrix based on a 2-D Gabor filter is given and the rules for feature merge are analyzed. Then, the influence of the scale parameter and quantization step on the detection performance of the scale co-occurrence matrix feature is discussed and verified. Next, the effect of diverse quantization strategy is presented. Last, the detailed extraction process of the proposed steganalysis feature is described. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms.

Object tracking plays a vital role in many computer vision systems applications, such as video surveillance, robotics, 3-D image reconstruction, medical imaging, human computer interface, etc. In many proposed approaches, feature-based object tracking is widely used due to its accuracy. Feature extraction and feature correspondence are two main components of feature-based object tracking. In our proposed method, we have used gray level co-occurrence matrix (GLCM) features for object tracking in thermal imagery. As spatial resolution of the thermal sensor is fairly coarse, it also implies that temperature scales are close but may not exactly be the same, which indicates the presence of mutually related pixels or group of pixels. The GLCM texture analysis is based on assumption that the texture information of an image is an average spatial relationship between the gray tones in the image. Thus, this similarity in spatial resolution properties makes GLCM features suitable for object tracking in thermal infrared imagery. Initially, the target blobs to be tracked are provided by object detection stage. Then, for a given target blob in a frame, we first calculate GLCM feature points and then find corresponding features in the next successive frame. The sum of squared differences between two feature point sets is calculated to find feature correspondence between two frames for object tracking. Simultaneously, the codebook of the center of the blobs for prediction of the target candidate region in the next frame is maintained in order to have robust object tracking under occlusions. GLCM-based object tracking in thermal imagery outperforms the color or LBP-based mean-shift approach. This algorithm is also able to track objects in split and merge condition. The accuracy of this algorithm also depends on the object detection stage, such as Kalman tracking.

Gray-level co-occurrence matrix (GLCM) is one of the most used methods for texture representation. As it can be computed only from gray-level images, a significant amount of information that could be provided by color is totally ignored. We propose a generalization of GLCM from gray level to hue saturation value color space, which we refer to as modified integrative color intensity co-occurrence matrix (MICICM). To reach such a generalization, a mapping function, which determines for each pixel value the bin it falls into, is needed. In many previous studies, this function uses a hard mapping where all pixel values that fall in a bin are considered as the same, regardless of their values. This presents a number of drawbacks. To fix them, we introduce a color and gray-level mapping scheme based on a set of weight assignment functions we propose. In our scheme, each pixel is mapped to more than one possible color (and gray-level) bin, to avoid the drawbacks of hard mapping. Although a fuzzy-based scheme has been recently proposed, our MICICM has successfully outperformed it and those of the state of the art. Our findings make several noteworthy contributions to image representation.

We present an image forensics method to detect least significant bit replacement steganographyattack. The proposed method provides fine-grained forensics features by using the hierarchical structurethat combines pixels correlation and bit-planes correlation. This is achieved via bit-plane decomposition anddifference matrices between the least significant bit-plane and each one of the others. Generated forensics fea-tures provide the susceptibility (changeability) that will be drastically altered when the cover image is embeddedwithdatatoformastegoimage.Wedevelopedastatisticalmodelbasedontheforensicsfeaturesandusedleastsquare support vector machine as a classifier to distinguish stego images from cover images. Experimentalresults show that the proposed method provides the following advantages. (1) The detection rate is noticeablyhigher than that of some existing methods. (2) It has the expected stability. (3) It is robust for content-preservingmanipulations, such as JPEG compression, adding noise, filtering, etc. (4) The proposed method providessatisfactory generalization capability.

We propose an innovative method that reduces the size of three-dimensional (3-D) volume data while preserving important features in the data. Our method quantifies the importance of features in the 3-D data based on gray level co-occurrence matrix texture analysis and represents the volume data using a simple mass-spring model. According to the measured importance value, blocks containing important features expand while other blocks shrink. After deformation, small features are exaggerated on deformed volume space, and more likely to survive during the uniform volume reduction. Experimental results showed that our method well preserved the small features of the original volume data during the reduction without any artifact compared with the previous methods. Although an additional inverse deformation process was required for the rendering of the deformed volume data, the rendering speed of the deformed volume data was much faster than that of the original volume data.

Spatial LSB±1 steganography changes smooth characteristics between adjoining pixels of the raw image. We present a novel steganalysis method for LSB±1 steganography based on feature vectors derived from the co-occurrence matrix in the spatial domain. We investigate how LSB±1 steganography affects the bit planes of an image and show that it changes more least significant bit (LSB) planes of it. The co-occurrence matrix is derived from an image in which some of its most significant bit planes are clipped. By this preprocessing, in addition to reducing the dimensions of the feature vector, the effects of embedding were also preserved. We compute the co-occurrence matrix in different directions and with different dependency and use the elements of the resulting co-occurrence matrix as features. This method is sensitive to the data embedding process. We use a Fisher linear discrimination (FLD) classifier and test our algorithm on different databases and embedding rates. We compare our scheme with the current LSB±1 steganalysis methods. It is shown that the proposed scheme outperforms the state-of-the-art methods in detecting the LSB±1 steganographic method for grayscale images.