We develop a model to identify the most likely regions for users to click in order to create graphical passwords in the PassPoints system. A PassPoints password is a sequence of points, chosen by a user in an image that is displayed on the screen. Our model predicts probabilities of likely click points; this enables us to predict the entropy of a click point in a graphical password for a given image. The model allows us to evaluate automatically whether a given image is well suited for the PassPoints system, and to analyze possible dictionary attacks against the system. We compare the predictions provided by our model to results of experiments involving human users. At this stage, our model and the experiments are small and limited; but they show that user choice can be modeled and that expansions of the model and the experiments are a promising direction of research.

In this paper, we introduce tamper detection techniques based on artifacts created by Color Filter Array (CFA) processing in most digital cameras. The techniques are based on computing a single feature and a simple threshold based classifier. The efficacy of the approach was tested over thousands of authentic, tampered, and computer generated images. Experimental results demonstrate reasonably low error rates.

Abstract-Digital single lens reflex cameras suffer from a well known sensor dust problem due to interchangeable lenses 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 (DSLR) camera at low false positive rates, even under heavy compression and downsampling.

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.

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Analogous to use of bullet scratches in forensic science, the authenticity of a digital image can be verified through the noise characteristics of an imaging sensor. In particular, photo-response non-uniformity noise (PRNU) has been used in source camera identification (SCI). However, this technique can be used maliciously to track or inculpate innocent people. To impede such tracking, PRNU noise should be suppressed significantly. Based on this motivation, we propose a counter forensic method to deceive SCI. Experimental results show that it is possible to impede PRNU-based camera identification for various imaging sensors while preserving the image quality.

ENF is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) due to changes in supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates. These fluctuations in luminance can be captured by video recordings. Accordingly, ENF can be estimated from such videos by analysis of steady content in the video scene. When videos are captured by using a rolling shutter sampling mechanism, as is done mostly with CMOS cameras, there is an idle period between successive frames. Consequently, a number of illumination samples of the scene are effectively lost due to the idle period. These missing samples affect ENF estimation, in the sense of the frequency shift caused and the power attenuation that results. This work develops an analytical model for videos captured using a rolling shutter mechanism. The model illustrates how the frequency of the main ENF harmonic varies depending on the idle period length, and how the power of the captured ENF attenuates as idle period increases. Based on this, a novel idle period estimation method for potential use in camera forensics that is able to operate independently of video frame rate is proposed. Finally, a novel time-of-recording verification approach based on use of multiple ENF components, idle period assumptions and interpolation of missing ENF samples is also proposed.