Identity theft facilitated by the use of morphed images is an emerging forensic concern. This scenario occurs when a digitally morphed image exhibits similarities to two accomplices. Given that the process of morphing alters the correlations between adjacent pixels and color channels, we propose the use of multidimensional co-occurrence matrices based on wavelet coefficients as features for detecting digitally morphed images. Experimental results show that the information about the frequencies at which wavelet coefficients and their prediction errors occur together are effective features in: a) accurately detecting digitally morphed images, b) uncovering distinctive traces left by various morphing methods in resulting images, and c) demonstrating the feasibility of developing universal morphing attack detectors.

The technologies making possible to change even the content of the digital media are developing fast. The demand for the development of mathematical and computational algorithms to determine the modifications on digital media has arisen and the studies in this area are gaining speed by the day. In this paper, we propose new statistical models for wavelet based transforms and try to differentiate between computer generated (CG) and natural images. Experimental results show that the features obtained from ridgelet and contourlet transforms are more successful than the wavelet based features in differentiating CG and natural images.

We propose statistical image models for wavelet-based transforms, investigate their use, and compare their relative merits within the context of digital image forensics. We consider the problems of 1) differentiating computer graphics images from photographic images, 2) source camera and source scanner identification, and 3) source artist identification from digital painting samples. The features obtained from ridgelet and contourlet transform-based image models almost always perform better than the features obtained from wavelet-based image models for the problems at hand. We outline properties of efficient image representation, relate these properties to wavelet-based transforms, and discuss the experimental results in relation to the model properties.

In this paper we present a novel method based on singular value decomposition (SVD) for forensic analysis of digital images. We show that image tampering distorts linear dependencies of image rows/columns and derived features can be accurate enough to detect image manipulations and digital forgeries. Extensive experiments show that the proposed approach can outperform the counterparts in the literature.

The various image-processing stages in a digital camera pipeline leave telltale footprints, which can be exploited as forensic signatures. These footprints consist of pixel defects, of unevenness of the responses in the CCD sensor, black current noise, and may originate from proprietary interpolation algorithms involved in color filter array [CFA]. Various imaging device (camera, scanner etc.) identification methods are based on the analysis of these artifacts. In this work, we set to explore three sets of forensic features, namely binary similarity measures, image quality measures and higher order wavelet statistics in conjunction with SVM classifier to identify the originating camera. We demonstrate that our camera model identification algorithm achieves more accurate identification, and that it can be made robust to a host of image manipulations. The algorithm has potential to discriminate camera units within the same model.

We investigate the use of chaotic-type features for recorded speech steganalysis. Considering that data hiding within a speech signal distorts the chaotic properties of the original speech signal, we design a steganalyzer that uses Lyapunov exponents and a fraction of false neighbors as chaotic features to detect the existence of a stegosignal. We also discuss the applicability of the proposed method to general audio.

Perturbed quantization (PQ) data hiding is almost undetectable with the current steganalysis methods. We briefly describe PQ and propose singular value decomposition (SVD)-based features for the steganalysis of JPEG-based PQ data hiding in images. We show that JPEG-based PQ data hiding distorts linear dependencies of rows/columns of pixel values, and proposed features can be exploited within a simple classifier for the steganalysis of PQ. The proposed steganalyzer detects PQ embedding on relatively smooth stego images with 70% detection accuracy on average for different embedding rates.

We first propose a novel content-independent distortion measurement method and use this methodology for digital audio steganalysis. Content-independent distortion measures are utilized as features for the classifier (steganalyzer) design. Experimental results show that the removal of content dependency from features enhances their discriminatory power.

In this work, we focus our interest on blind source camera identification problem by extending our results in the direction of M. Kharrazi et al. (2004). The interpolation in the color surface of an image due to the use of a color filter array (CFA) forms the basis of the paper. We propose to identify the source camera of an image based on traces of the proprietary interpolation algorithm deployed by a digital camera. For this purpose, a set of image characteristics are defined and then used in conjunction with a support vector machine based multi-class classifier to determine the originating digital camera. We also provide initial results on identifying source among two and three digital cameras.

We present a compression technique that provides progressive transmission as well as lossless and near-lossless compression in a single framework. The proposed technique produces a bit stream that results in a progressive, and ultimately lossless, reconstruction of an image similar to what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides near-lossless reconstruction with respect to a given bound, after decoding of each layer of the successively refinable bit stream. We formulate the image data-compression problem as one of successively refining the probability density function (pdf) estimate of each pixel. Within this framework, restricting the region of support of the estimated pdf to a fixed size interval then results in near-lossless reconstruction. We address the context-selection problem, as well as pdf-estimation methods based on context data at any pass. Experimental results for both lossless and near-lossless cases indicate that the proposed compression scheme, that innovatively combines lossless, near-lossless, and progressive coding attributes, gives competitive performance in comparison with state-of-the-art compression schemes.

We present techniques for steganalysis of images that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. Our hypothesis is that steganographic schemes leave statistical evidence that can be exploited for detection with the aid of image quality features and multivariate regression analysis. To this effect image quality metrics have been identified based on the analysis of variance (ANOVA) technique as feature sets to distinguish between cover-images and stego-images. The classifier between cover and stego-images is built using multivariate regression on the selected quality metrics and is trained based on an estimate of the original image. Simulation results with the chosen feature set and well-known watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images.

We present a novel technique for steganalysis of images that have been subjected to least significant bit (LSB) type steganographic algorithms. The seventh and eighth bit planes in an image are used for the computation of several binary similarity measures. The basic idea is that the correlation between the bit planes as well the binary texture characteristics within the bit planes differs between a stego-image and a cover-image. These telltale marks can be used to construct a steganalyzer, that is, a multivariate regression scheme to detect the presence of a steganographic message in an image.

A novel image compression technique is presented that incorporates progressive transmission and near-lossless compression in a single framework. Experimental performance of the proposed coder proves to be competitive with the state-of-the-art compression schemes.

We present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. We show that watermarking schemes leave statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some image quality metrics as the feature set to distinguish between watermarked and unwatermarked images and furthermore distinguish between different watermarking techniques. To identify specific quality measures that provide the best discriminative power, we use analysis of variance (ANOVA) techniques. Multivariate regression analysis is then used on the selected quality metrics to build an optimal classifier using a set of test images and their blurred versions. Simulation results with a specific feature set and some well-known and publicly available watermarking techniques indicate that our approach is able to accurately distinguish with high accuracy between images marked by different watermarking techniques.