Husrev T. Sencar

Blind source camera identification

An interesting problem in digital forensics is that given a digital image, would it be possible to identify the camera model which was used to obtain the image. In this paper we look at a simplified version of this problem by trying to distinguish between images captured by a limited number of camera models. We propose a number of features which could be used by a classifier to identify the source camera of an image in a blind manner. We also provide experimental results and show reasonable accuracy in distinguishing images from the two and five different camera models using the proposed features.

Source camera identification based on CFA interpolation

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.

A secure biometric authentication scheme based on robust hashing

In this paper, we propose a secure biometric based authentication scheme which fundamentally relies on the use of a robust hash function. The robust hash function is a one-way transformation tailored specifically for each user based on their biometrics. The function is designed as a sum of properly weighted and shifted Gaussian functions to ensure the security and privacy of biometric data. We discuss various design issues such as scalability, collision-freeness and security. We also provide test results obtained by applying the proposed scheme to ORL face database by designating the biometrics as singular values of face images.

Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust

Digital single lens reflex cameras suffer from a well-known sensor dust problem due to interchangeable lenses that they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust-spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter-dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex camera at low false positive rates, even under heavy compression and downsampling.

Performance study of common image steganography and steganalysis techniques

We investigate the performance of state of the art univer- sal steganalyzers proposed in the literature. These universal stega- nalyzers are tested against a number of well-known steganographic embedding techniques that operate in both the spatial and transform domains. Our experiments are performed using a large data set of JPEG images obtained by randomly crawling a set of publicly avail- able websites. The image data set is categorized with respect to size, quality, and texture to determine their potential impact on ste- ganalysis performance. To establish a comparative evaluation of techniques, undetectability results are obtained at various embed- ding rates. In addition to variation in cover image properties, our comparison also takes into consideration different message length definitions and computational complexity issues. Our results indi- cate that the performance of steganalysis techniques is affected by the JPEG quality factor, and JPEG recompression artifacts serve as a source of confusion for almost all steganalysis techniques.

Classification of digital camera-models based on demosaicing artifacts

We utilize traces of demosaicing operation in digital cameras to identify the source camera-model of a digital image. To identify demosaicing artifacts associated with different camera-models, we employ two methods and define a set of image characteristics which are used as features in designing classifiers that distinguish between digital camera-models. The first method tries to estimate demosaicing parameters assuming linear model while the second one extracts periodicity features to detect simple forms of demosaicing. To determine the reliability of the designated image features in differentiating the source camera-model, we consider both images taken under similar settings at fixed sceneries and images taken under independent conditions. In order to show how to use these methods as a forensics tool, we consider several scenarios where we try to (i) determine which camera-model was used to capture a given image among three, four, and five camera-models, (ii) decide whether or not a given image was taken by a particular camera-model among very large number of camera-models (in the order of hundreds), and (iii) more reliably identify the individual camera, that captured a given image, by incorporating demosaicing artifacts with noise characteristics of the imaging sensor of the camera.

Improvements on Sensor Noise Based Source Camera Identification

In a novel method for identifying the source camera of a digital image is proposed. The method is based on first extracting imaging sensors pattern noise from many images and later verifying its presence in a given image through a correlative procedure. In this paper, we investigate the performance of this method in a more realistic setting and provide results concerning its detection performance. To improve the applicability of the method as a forensic tool, we propose an enhancement over it by also verifying that class properties of the image in question are in agreement with those of the camera. For this purpose, we identify and compare characteristics due to demosaicing operation. Our results show that the enhanced method offers a significant improvement in the performance.

New Features to Identify Computer Generated Images

Discrimination of computer generated images from real images is becoming more and more important. In this paper, we propose the use of new features to distinguish computer generated images from real images. The proposed features are based on the differences in the acquisition process of images. More specifically, traces of demosaicking and chromatic aberration are used to differentiate computer generated images from digital camera images. It is observed that the former features perform very well on high quality images, whereas the latter features perform consistently across a wide range of compression values. The experimental results show that proposed features are capable of improving the accuracy of the state-of-the-art techniques.

Tamper Detection Based on Regularity of Wavelet Transform Coefficients

Powerful digital media editing tools make producing good quality forgeries very easy for almost anyone. Therefore, proving the authenticity and integrity of digital media becomes increasingly important. In this work, we propose a simple method to detect image tampering operations that involve sharpness/blurriness adjustment. Our approach is based on the assumption that if a digital image undergoes a copy-paste type of forgery, average sharpness/blurriness value of the forged region is expected to be different as compared to the non-tampered parts of the image. The method of estimating sharpness/blurriness value of an image is based on the regularity properties of wavelet transform coefficients which involves measuring the decay of wavelet transform coefficients across scales. Our preliminary results show that the estimated sharpness/blurriness scores can be used to identify tampered areas of the image.

Cover Selection for Steganographic Embedding

The primary goal of image steganography techniques has been to maximize embedding rate while minimizing the detectability of the resulting stego images against steganalysis techniques. However, one particular advantage of steganography, as opposed to other information hiding techniques, is that the embedder has the freedom to choose a cover image that result in the least detectable stego image. This resource has largely remained unexploited in the proposed embedding techniques. In this paper, we study the problem of cover selection by investigating three scenarios in which the embedder has either no knowledge, partial knowledge, or full knowledge of the steganalysis technique. For example, we illustrate through experiments how simple statistical measures could help embedder minimize detectability, at times by 65%, in the partial knowledge case.