Abstract:
An image processing apparatus includes: an image dividing unit that divides a first image and a second image into a plurality of first divided images and a plurality of second divided images, respectively; a corresponding divided image extract unit that extracts the second divided images corresponding to the first divided images; a difference extract unit that extracts a difference image having a difference generated due to additional entry or extraction between pixels included within each of the first divided images and pixels included within each of the second divided images; a first change unit that, in a case where the first divided images has such a movement relationship that it can be moved relative to the second divided images, changes a color of the second divided images into a first color; a second change unit that changes a color of the difference image extracted by the difference extract unit into a second color; and an image output unit that outputs the second divided images with the color changed by the first change unit and the difference image with the color changed by the second change unit.
Abstract:
A digital data false alteration detection program causes a computer to execute (a) a step (S1) of dividing digital data into a plurality of smaller block data, (b) a step (S2) of extracting noise inherent to a digital data acquisition device for each of the small block data, (c) a step (S3) of calculating correlation of the noise between adjacent small block data, and (d) a step (S4) of detecting small block data having noise correlation lower than a level predetermined for the surrounding small block data, as falsely altered data.
Abstract:
An image forensics system estimates a camera response function (CRF) associated with a digital image, and compares the estimated CRF to a set of rules and compares the estimated CRF to a known CRF. The known CRF is associated with a make and a model of an image sensing device. The system applies a fusion analysis to results obtained from comparing the estimated CRF to a set of rules and from comparing the estimated CRF to the known CRF, and assesses the integrity of the digital image as a function of the fusion analysis.
Abstract:
A counterfeit identification performance attribute (SIPA) sensitivity to changes in resolution of the image for features of an image is determined. The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit.
Abstract:
The present disclosure is generally directed to a method and computing device for determining whether a mark is genuine. According to various embodiments, a computing device (or logic circuitry thereof) uses unintentionally-produced artifacts within a genuine mark to define an identifiable electronic signature, and extracts certain location identifiers corresponding to certain measured features of the signature in order to enhance the ease and speed with which numerous genuine signatures can be searched and compared with signatures of candidate marks.
Abstract:
Image integrity in an archive can be verified using document characteristics. Embodiments of the invention provide a way to verify the integrity of a stored document image by determining document characteristics, which can also be embedded in the image file. Before allowing access to the image file by an application, the characteristics data from an image analysis can be compared to either or both of, characteristics information otherwise stored, or embedded characteristics data. The embedded data can optionally be encrypted. In example embodiments the data can include a result of an optical character recognition of contents of the document, a length of data describing the image, a percentage of a specified color of pixels in the image, or a checksum. Example embedding techniques can include those making use of a tagged image file format (TIFF) header, a steganographic watermark, or an image artifact.
Abstract:
A method for watermarking of video content is provided. An averaged scene image is computed for each scene of video content by performing averaging of frames present in each scene of video content. For each averaged scene image a set of random numbers are generated using a secret key to identify pixels at random locations of the averaged scene image. The secret key is associated with a watermark pattern corresponding to each averaged scene image. The identified pixels in each averaged scene image are mapped to each pixel of corresponding watermark pattern to obtain respective mapped pixels. Using respective mapped pixels, values of verification information are fetched and assigned using predetermined rules. The values of verification information are arranged to obtain first visual cryptographic share of watermark pattern for each averaged scene image.
Abstract:
This image feature extraction device extracts, from an image, an image feature that makes it possible to adjust the balance between identification capability and robustness, which are the scales of the capability of determining identity of images. This image feature extraction device executes hierarchical quantization to calculate quantization indexes of a plurality of hierarchies in accordance with a previously defined hierarchical quantization method for each quantization target region of an image, and outputs a hierarchical quantization index code, which is an encoding allowing unique specification of the quantization indexes of the respective hierarchies of each quantization target region.
Abstract:
There is provided a method of identifying User-Created Content (UCC) using an image identifier, including combining a still image with the image identifier, requesting registration of UCC corresponding to the still image combined with the image identifier, and combining a UCC identifier, issued in response to the request, with the still image combined with the image identifier, thus generating final UCC.
Abstract:
A method for characterization of objects has the steps of: a) describing an object with an elliptical self-adjoint eigenvalue problem in order to form an isometrically invariant model; b) determining eigenvalues of the eigenvalue problem; and c) characterizing the object by the eigenvalues.