Abstract:
Techniques described herein are generally related to steganalysis of suspect media. Steganalysis techniques may include receiving instances of suspect media as input for steganalytic processing. A first set of quantized blocks of data elements may be identified within the media, with this first set of blocks being eligible to be embedded with steganographic data. A second set of quantized blocks of data elements may be identified within the media, with this second set of blocks being ineligible to be embedded with steganographic data. The steganalysis techniques may requantize the first and second blocks. In turn, these techniques may compare statistics resulting from requantizing the first block with statistics resulting from requantizing the second block. The steganalysis techniques may then assess whether the first block of data elements is embedded with steganographic features based on how the statistics of the second blocks compare with the statistics of the first blocks.
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 producing an assured image acquires image data and segments the image data into one or more spatial regions. One or more quality measures is calculated from the image data that is within the one or more spatial regions. Secure assurance data is produced that is representative of the one or more quality measures and the image data. The secure assurance data is associated with the image data to produce the assured image.
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:
A method and apparatus are provided for analyzing, identifying, and comparing images. The method can be used with any visually-displayed medium that is represented in any type of color space. An identified image can be authenticated, registered, marked, compared to another image, or recognized using the method and apparatus according to the present invention. At least one characteristic of an image's color space is selected and determined to generate a unique description of the image. This identification information is then used to compare different identified images to determine if they are identical according to a set of predetermined criteria. The predetermined criteria can be adjusted to permit the identification of images that are identical in part. In the preferred embodiment of the present invention, a software search application, such as a search engine or a spider, is used to locate and retrieve an image to be identified from an electronic network. A notification alarm is triggered when a duplicate image is located. In one embodiment, the present invention is implemented using a computer. One or more software applications, software modules, firmware, and hardware, or any combination thereof, are used to determine the identification information for the selected image characteristics, search for images, provide notification of identical images, and to generate a database of identified images.
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.