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:
An area where development processing is irregular in imaging data are detected as an irregularity area. At least two areas having approximate feature amounts in the imaging data are detected as approximate areas. An altered region in the imaging data is specified from the approximate areas based on the irregularity area and the approximate areas.
Abstract:
The present disclosure is generally directed to a method and computing device for determining whether a mark is genuine. According to various implementations, a computing device (or logic circuitry thereof) uses unintentionally-produced artifacts within a genuine mark to define an identifiable electronic signature, extracts certain attributes of the signature (such as deviation from the mean value for each band of the signature), and assigns numerical values to the extracted attributes in order to create a hash identifier that is significantly smaller than the electronic signature itself. The hash identifier is then used as an index for a database of electronic signatures (of genuine marks) to enhance the ease and speed with which numerous genuine signatures can be searched (e.g., in a database) and compared with signatures (of candidate marks.
Abstract:
Methods and systems of detecting tampering in a digital image includes using hybrid large feature mining to identify one or more regions of an image in which tampering has occurred. Detecting tampering in a digital image with hybrid large feature mining may include spatial derivative large feature mining and transform-domain large feature mining. In some embodiments, known ensemble learning techniques are employed to address high feature dimensionality. detecting inpainting forgery includes mining features of a digital image under scrutiny based on a spatial derivative, mining one or more features of the digital image in a transform-domain; and detecting inpainting forgery in the digital image under scrutiny at least in part by the features mined based on the spatial derivative and at least in part by the features mined in the transform-domain.
Abstract:
A method of detecting tampering in a compressed digital image includes extracting one or more neighboring joint density features from a digital image under scrutiny and extracting one or more neighboring joint density features from an original digital image. The digital image under scrutiny and the original digital image are decompressed into a spatial domain. Tampering in the digital image under scrutiny is detected based on at least one difference in a neighboring joint density feature of the digital image under scrutiny and a neighboring joint density feature of the original image. In some embodiments, detecting tampering in the digital image under scrutiny includes detecting down-recompression of at least a portion of the digital image. In some embodiments, detecting tampering in the digital image includes detecting inpainting forgery in the same quantization.
Abstract:
Pattern information of a color filter array and algorithm information of color interpolation processing used in photography of input image data are obtained. Comparative image data is generated from the input image data using the pattern information and algorithm information. The comparative image data and input image data are compared to detect alteration of the input image data.
Abstract:
A CFA pattern is extracted from captured image data for each first unit region. A first altered region is detected from disturbance of the periodicity of the CFA pattern, and the first altered region is an image region in which copying has been performed from image data different from the captured image data to the captured image data. The feature amount of the captured image data is extracted for each second unit region different in size from the first unit region. The feature amounts are compared for each second unit region to detect a second altered region, and the second altered region is an image region in which copying has been performed from the captured image data to the captured image data. Information concerning the first and second altered regions are output as alteration detection results in the captured image data.
Abstract:
An image processing method and apparatus for tamper proofing are proposed. The method includes: acquiring a first robust feature representation of a first set of robust feature points for an original image and a second robust feature representation of a second set of robust feature points for an image to be detected, respectively; matching the first robust feature representation with the second robust feature representation so as to acquire mismatching feature points; and determining whether the image to be detected has been tampered with relative to the original image based on a distribution characteristic of the mismatching feature points. With the embodiments of the invention, the distribution characteristic of the mismatching feature points is analyzed based on the robust feature representations of the original image and the image to be detected, so that conventional operations on the image can be distinguished effectively from tampering in the image with sufficient robustness.
Abstract:
This invention provides a technique of preventing determination of image alteration when digital image data has undergone, e.g., rotation without any substantial change in contents. To do this, an area separation processing unit separates image data into areas. For each of the separated areas, an area feature value calculator calculates an area feature value independent of the coordinate information of the image. An area order sorter sorts the separated areas in accordance with the calculated area feature values. A validation data generation processing unit generates validation data based on the sort result.
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.