Patents by Inventor Qingzhong Liu

Qingzhong Liu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Exposing inpainting image forgery under combination attacks with hybrid large feature mining
    Patent number: 10032265
    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.
    Type: Grant
    Filed: September 1, 2016
    Date of Patent: July 24, 2018
    Assignee: Sam Houston State University
    Inventor: Qingzhong Liu
  • Rich feature mining to combat anti-forensics and detect JPEG down-recompression and inpainting forgery on the same quantization
    Patent number: 9922389
    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.
    Type: Grant
    Filed: June 10, 2015
    Date of Patent: March 20, 2018
    Assignee: Sam Houston State University
    Inventor: Qingzhong Liu
  • EXPOSING INPAINTING IMAGE FORGERY UNDER COMBINATION ATTACKS WITH HYBRID LARGE FEATURE MINING
    Publication number: 20170091588
    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.
    Type: Application
    Filed: September 1, 2016
    Publication date: March 30, 2017
    Inventor: Qingzhong Liu
  • RICH FEATURE MINING TO COMBAT ANTI-FORENSICS AND DETECT JPEG DOWN-RECOMPRESSION AND INPAINTING FORGERY ON THE SAME QUANTIZATION
    Publication number: 20160132985
    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.
    Type: Application
    Filed: June 10, 2015
    Publication date: May 12, 2016
    Inventor: Qingzhong Liu
  • Steganalysis with neighboring joint density
    Patent number: 8965038
    Abstract: Systems and methods for detecting hidden messages and information in digital files are described. In an embodiment, a method of detecting steganography in a compressed digital image includes extracting neighboring joint density features from the image under scrutiny. Steganography in the image may be detected based on differences in a neighboring joint density feature of the image.
    Type: Grant
    Filed: February 1, 2013
    Date of Patent: February 24, 2015
    Assignee: Sam Houston University
    Inventor: Qingzhong Liu
  • STEGANALYSIS WITH NEIGHBORING JOINT DENSITY
    Publication number: 20130208941
    Abstract: Systems and methods for detecting hidden messages and information in digital files are described. In an embodiment, a method of detecting steganography in a compressed digital image includes extracting neighboring joint density features from the image under scrutiny. Steganography in the image may be detected based on differences in a neighboring joint density feature of the image.
    Type: Application
    Filed: February 1, 2013
    Publication date: August 15, 2013
    Inventor: Qingzhong Liu