Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method: Theory and Applications

Abstract

Geometric transformations, such as resizing and rotation, are almost always needed when two or more images are spliced together to create convincing image forgeries. In recent years, researchers have developed many digital forensic techniques to identify these operations. Most previous works in this area focus on the analysis of images that have undergone single geometric transformations, e.g., resizing or rotation. In several recent works, researchers have addressed yet another practical and realistic situation: successive geometric transformations, e.g., repeated resizing, resizing-rotation, rotation-resizing, and repeated rotation. We will also concentrate on this topic in this paper. Specifically, we present an in-depth analysis in the frequency domain of the second-order statistics of the geometrically transformed images. We give an exact formulation of how the parameters of the first and second geometric transformations influence the appearance of periodic artifacts. The expected positions of characteristic resampling peaks are analytically derived. The theory developed here helps to address the gap left by previous works on this topic and is useful for image security and authentication, in particular, the forensics of geometric transformations in digital images. As an application of the developed theory, we present an effective method that allows one to distinguish between the aforementioned four different processing chains. The proposed method can further estimate all the geometric transformation parameters. This may provide useful clues for image forgery detection.

Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis

“…The process of image rotation is often accompanied by an interpolation operation, which makes the cyclic spectral amplitude of covariance (second-order statistic) show a certain periodicity [3,9,10]. Assuming the original image is f (x 1 , x 2 ), the rotated image is as follows:…”

“…However, every coin has two sides; with the popularization of various image editing tools, people can recreate images in their own way, but the authenticity of the image cannot be guaranteed, and fake images affect people's life in different aspects. Therefore, in order to identify the authenticity of images, image forensics technology has come into being, and blind image forensics is widely applied because it does not need prior information [1][2][3]. In the process of image stitching and tampering, the forged area is usually resampled by rotation, scaling, and other processes to make it more realistic from a geometric perspective [3].…”

“…Therefore, in order to identify the authenticity of images, image forensics technology has come into being, and blind image forensics is widely applied because it does not need prior information [1][2][3]. In the process of image stitching and tampering, the forged area is usually resampled by rotation, scaling, and other processes to make it more realistic from a geometric perspective [3]. Therefore, the detection of resampled images has great significance in the field of image forensics.…”

“…Vazquez-Padin et al[9] applied a second-order cyclostationary method to detect the periodicity caused by resampling. Chen et al [3,10] improved the theoretical derivation and experimental results of Vazquez-Padin et al [9], and the further derivation of double resampling was presented. However, it resulted in larger errors when the rotation angle was small.…”

Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

“…2009; Liu and Chen 2014;Korus and Huang 2016;Chen et al 2017;Dong et al 2015]. Consequently, digital image forensics, which aims to identify the original source of an image or determine whether or not the content of an image has been modified, has become increasingly important.…”