Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The~experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100\% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296; Sensors 2018, 18(4), 129

RTP timestamp steganography detection method

A histogram cosine similarity matching method for real-time transport protocol (RTP) timestamp difference vectors and a clustering method of the area between the best-fit curves of 2 RTP timestamp difference sequences are presented. These 2 methods realize timestamp-based least significant bit (LSB) steganography detection respectively. A clustering analysis of the area between the 5th-degree polynomial best-fit curves with message windows w of 20, 50, 100, and 200 was conducted. The results indicated that when the message window w was 100, the result was the best when the characteristic extraction time was shortest, and the initial clustering accuracy was 84.5%. Through further analysis, the clustering accuracy was increased to 100% in the 2nd round of clustering based on whether the mean distance from a data point in an initial cluster to each cluster center was changed

Forensic research on detecting seam carving in digital images

Digital images have been playing an important role in our daily life for the last several decades. Naturally, image editing technologies have been tremendously developed due to the increasing demands. As a result, digital images can be easily manipulated on a personal computer or even a cellphone for many purposes nowadays, so that the authenticity of digital images becomes an important issue. In this dissertation research, four machine learning based forensic methods are presented to detect one of the popular image editing techniques, called ‘seam carving’. To reveal seam carving applied to uncompressed images from the perspective of energy distribution change, an energy based statistical model is proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A support vector machine (SVM) based classifier is employed to determine whether an image has been seam carved or not. In the second work, an advanced feature model is presented for seam carving detection by investigating the statistical variation among neighboring pixels. Comprised with three types of statistical features, i.e., LDP features, Markov features, and SPAM features, the powerful feature model significantly improved the state-of-the-art accuracy in detecting low carving rate seam carving. After the feature selection by utilizing SVM based recursive feature elimination (SVM-RFE), with a small amount of features selected from the proposed model the overall performance is further improved. Combining above mentioned two works, a hybrid feature model is then proposed as the third work to further boost the accuracy in detecting seam carving at low carving rate. The proposed model consists of two sets of features, which capture energy change and neighboring relationship variation respectively, achieves remarkable performance on revealing seam carving, especially low carving rate seam carving, in digital images. Besides these three hand crafted feature models, a deep convolutional neural network is designed for seam carving detection. It is the first work that successfully utilizes deep learning technology to solve this forensic problem. The experimental works demonstrate their much more improved performance in the cases where the amount of seam carving is not serious. Although these four pieces of work move the seam carving detection ahead substantially, future research works with more advanced statistical model or deep neural network along this line are expected

Secure Image-authentication Schemes with Hidden Double Random-phase Encoding

We present a new image-authentication algorithm based on binary-quantified double random-phase encoding (DRPE) and a discrete cosine transformation (DCT) domain watermarking scheme. The image is encrypted using a DRPE scheme, in which only the phase part of the encoded image is preserved. Then, this phase image is quantified to become a binary image by giving 0 to these phase values that are less than 0 and setting others to 1. Then, the quantified binary image is secretly inserted into a host image with a DCT-domain watermarking algorithm. During image authentication, the receiver gets the binary image from the watermarked image using an inverse DCT operation and codes 0 values as -pi and values of 1 as pi to create a phase image. Finally, the input image is decoded from the retrieved phase image based on a double random phase decryption technique and is further authenticated using a nonlinear cross-correlation method. The present image-authentication algorithm offers an additional layer of system security because the hidden binary image reveals no information that is from the original image. Moreover, the image decrypted from the retrieved phase image cannot be easily recognized with naked eyes. However, it can be successfully authenticated by nonlinear cross-correlation, even in the face of attacks including noise attacks, filtering attacks, partial occlusion attacks, or geometric transformation attacks to the watermarked image. Our simulation results demonstrated the capability of the proposed image-authentication technique.1