Lin Li

Jennifer Newman, Li Lin, Wenhao Chen et al.

In this paper, we present a new reference dataset simulating digital evidence for image steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10 different phone models from 24 distinct devices, with detailed provenanced data comprising a wide range of ISO and exposure settings, EXIF data, message information, embedding rates, etc. We develop a camera app, Cameraw, specifically for data acquisition, with multiple images per scene, saving simultaneously in both DNG and high-quality JPEG formats. Stego images are created from these original images using selected mobile stego apps through a careful process of reverse engineering. StegoAppDB contains cover-stego image pairs including for apps that resize the stego dimensions. We retainthe original devices and continue to enlarge the database, and encourage the image forensics community to use StegoAppDB. While designed for steganography, we discuss uses of this publicly available database to other digital image forensic topics.

Chengyao Du, Jingling Yuan, Jiansheng Dong et al.

Panoramic video is a sort of video recorded at the same point of view to record the full scene. With the development of video surveillance and the requirement for 3D converged video surveillance in smart cities, CPU and GPU are required to possess strong processing abilities to make panoramic video. The traditional panoramic products depend on post processing, which results in high power consumption, low stability and unsatisfying performance in real time. In order to solve these problems,we propose a real-time panoramic video stitching framework.The framework we propose mainly consists of three algorithms, LORB image feature extraction algorithm, feature point matching algorithm based on LSH and GPU parallel video stitching algorithm based on CUDA.The experiment results show that the algorithm mentioned can improve the performance in the stages of feature extraction of images stitching and matching, the running speed of which is 11 times than that of the traditional ORB algorithm and 639 times than that of the traditional SIFT algorithm. Based on analyzing the GPU resources occupancy rate of each resolution image stitching, we further propose a stream parallel strategy to maximize the utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency of this strategy is improved by 1.6-2.5 times, and it can make full use of GPU resources. The performance of the system accomplished in the paper is 29.2 times than that of the former embedded one, while the power dissipation is reduced to 10W.

Wenhao Chen, Li Lin, Min Wu et al.

Digital image forensics is a young but maturing field, encompassing key areas such as camera identification, detection of forged images, and steganalysis. However, large gaps exist between academic results and applications used by practicing forensic analysts. To move academic discoveries closer to real-world implementations, it is important to use data that represent "in the wild" scenarios. For detection of stego images created from steganography apps, images generated from those apps are ideal to use. In this paper, we present our work to perform steg detection on images from mobile apps using two different approaches: "signature" detection, and machine learning methods. A principal challenge of the ML task is to create a great many of stego images from different apps with certain embedding rates. One of our main contributions is a procedure for generating a large image database by using Android emulators and reverse engineering techniques. We develop algorithms and tools for signature detection on stego apps, and provide solutions to issues encountered when creating ML classifiers.