Sam Houston State University

Sam Houston University

Qingzhong Liu

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.

Exposing inpainting image forgery under combination attacks with hybrid large feature mining

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.

Rich feature mining to combat anti-forensics and detect JPEG down-recompression and inpainting forgery on the same quantization

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.

Growing community of inventors

Huntsville, TX, United States of America

Qingzhong Liu