A large portion of digital image data available today is acquired using digital cameras or scanners. While cameras
allow digital reproduction of natural scenes, scanners are often used to capture hardcopy art in more controlled
scenarios. This paper proposes a new technique for non-intrusive scanner model identification, which can be
further extended to perform tampering detection on scanned images. Using only scanned image samples that
contain arbitrary content, we construct a robust scanner identifier to determine the brand/model of the scanner
used to capture each scanned image. The proposed scanner identifier is based on statistical features of scanning
noise. We first analyze scanning noise from several angles, including through image de-noising, wavelet analysis,
and neighborhood prediction, and then obtain statistical features from each characterization. Experimental
results demonstrate that the proposed method can effectively identify the correct scanner brands/models with
Digital elevation maps (DEMs) provide a digital representation of 3-D terrain information. In civilian applications, high-precision DEMs carry a high commercial value owing to the large amount of effort in acquiring them; and in military applications, DEMs are often used to represent critical geospatial information in sensitive operations. These call for new technologies to prevent unauthorized distribution and to trace traitors in the event of information leak related to DEMs. In this paper, we propose a new digital fingerprinting technique to protect DEM data from illegal re-distribution. The proposed method enables reliable detection of fingerprints from both 3-D DEM data set and its 2-D rendering, whichever format that is available to a detector. Our method starts with extracting from a DEM a set of critical contours either corresponding to important topographic features of the terrain or having application-dependent importance. Fingerprints are then embedded into these critical contours by employing parametric curve modeling and spread spectrum embedding. Finally, a fingerprinted DEM is constructed to incorporate the marked 2-D contours. Through experimental results, we demonstrate the robustness of the proposed method against a number of challenging attacks applied to either DEMs or their contour representations.
Whether in the domain of audio, video or finance, our world tends to become increasingly digital. However, for diverse reasons, the transition from analog to digital is often much extended in time, and proceeds by long steps (and sometimes never completes). One such step is the conversion of information on analog media to digital information. We focus in this paper on the conversion (scanning) of printed documents to digital images. Analog media have the advantage over digital channels that they can harbor much imperceptible information that can be used for fraud detection and forensic purposes. But this secondary information usually fails to be retrieved during the conversion step. This is particularly relevant since the Check-21 act (Check Clearing for the 21st Century act) became effective in 2004 and allows images of checks to be handled by banks as usual paper checks. We use here this situation of check scanning as our primary benchmark for graphic security features after scanning. We will first present a quick review of the most common graphic security features currently found on checks, with their specific purpose, qualities and disadvantages, and we demonstrate their poor survivability after scanning in the average scanning conditions expected from the Check-21 Act. We will then present a novel method of measurement of distances between and rotations of line elements in a scanned image: Based on an appropriate print model, we refine direct measurements to an accuracy beyond the size of a scanning pixel, so we can then determine expected distances, periodicity, sharpness and print quality of known characters, symbols and other graphic elements in a document image. Finally we will apply our method to fraud detection of documents after gray-scale scanning at 300dpi resolution. We show in particular that alterations on legitimate checks or copies of checks can be successfully detected by measuring with sub-pixel accuracy the irregularities inherently introduced by the illegitimate process.
Hiding data in binary images can facilitate the authentication and annotation of important document images in digital domain. A representative approach is to first identify pixels whose binary color can be flipped without introducing noticeable artifacts, and then embed one bit in each non-overlapping block by adjusting the flippable pixel values to obtain the desired block parity. The distribution of these flippable pixels is highly uneven across the image, which is handled by random shuffling in the literature. In this paper, we revisit the problem of data embedding for binary images and investigate the incorporation of a most recent steganography framework known as the wet paper coding to improve the embedding capacity. The wet paper codes naturally handle the uneven embedding capacity through randomized projections. In contrast to the previous approach, where only a small portion of the flippable pixels are actually utilized in the embedding, the wet paper codes allow for a high utilization of pixels that have high flippability score for embedding, thus giving a significantly improved embedding capacity than the previous approach. The performance of the proposed technique is demonstrated on several representative images. We also analyze the perceptual impact and capacity-robustness relation of the new approach.