Content-aware image resizing is the process of adjusting the size of an image while preserving its important content. Image resizing is used to overcome diversity in resolutions between modules, such as display devices and applications, and can thus be deliberately exploited to distort or remove original content; therefore, detecting such tampering has become an important topic in forensics. This paper proposes a deep neural network architecture to capture subtle local artifacts caused by seam-based image resizing. Unlike past approaches that only classified two classes, our approach is the first attempt to solve a given forensic task with three-class classification: original, seam insertion, and seam carving. The experimental results show that our work performs better than the handcrafted feature-based method and networks designed for different forensic tasks.

Double compression (DC) involves compressing a video twice with the video codec. DC usually occurs when a compressed video is manipulated, because most videos are saved in the compression format after manipulating. Given this principle, if we can detect DC traces in videos, we can determine whether a video has been manipulated. Prior works have proposed methods for detecting DC of H.264 videos; however, they have limitations in that they can detect DC only for a limited dataset and set of compression parameters. To overcome these limitations, we propose a two-stream neural network that incorporates two components that analyze intra-coded frames and predictive frames. We explain why the proposed two-stream neural network can detect DC traces in videos more accurately than the prior method, and we demonstrate that the proposed network can detect DC videos that have various contents and parameters through extensive experiments.

As computer graphics technology advances, it is becoming increasingly difficult to determine whether a given picture was taken by camera or via computer graphics. In this work, we propose a method to using simple CNN structures to identify photorealistic computer graphics (PRCG) using convolutional neural networks (CNN). This network trained to identify the source of image patches. We showed the network without pooling layer showed 98.2% accuracy, which is 2.1% higher than the result of using conventional object-recognition network. Testing random patches from image, the accuracy of identifying image reached 98.5%. Furthermore, it is possible to detect the photograph-PRCG synthesized regions from the image.