Learn More
With the rapid development of digital image editing tools, the authenticity of digital images becomes questionable in recent years. Image tampering detection is a technology that detects tampered images by using intrinsic image regularities. However, existing intrinsic image regularities are designed for one specific type of tampering operations. When(More)
We propose in this paper a novel technique to correlate statistical image noise features with three EXchangeable Image File format (EXIF) header features for manipulation detection. By formulating each EXIF feature as a weighted sum of selected statistical image noise features using sequential floating forward selection, the weights are then solved as a(More)
EXchangeable Image File format (EXIF) is a metadata header containing shot-related camera settings such as aperture, exposure time, ISO speed etc. These settings can affect the photo content in many ways. In this paper, we investigate the underlying EXIF-Image correlation and propose a novel model, which correlates image statistical noise features with(More)
In this paper, we propose a fast online learning framework for landmark recognition based on single hidden layer feedforward neural networks (SLFNs). Conventional landmark recognition frameworks generally assume that all images are available at hand to train the classifier. However, in real world applications, people may encounter the issue that the(More)
Along with the rapid development of mobile terminal devices, landmark recognition applications based on mobile devices have been widely researched in recent years. Due to the fast response time requirement of mobile users, an accurate and efficient landmark recognition system is thus urgent for mobile applications. In this paper, we propose a landmark(More)
This paper proposes a novel framework for image splicing detection based on the inconsistency in the blur degree and depth information of an image. Firstly, the blur kernels of image blocks (local blur kernels) are estimated. Next, a multi-step reblurring technique is used to measure the relative blur degrees of the local blur kernels. The relative blur(More)
This paper presents a codebook learning based mobile landmark recognition technique based on context information that is acquired from mobile devices. Previous codebook learning methods are mainly developed on nonmobile platforms such as desktop PC, hence underutilize context features such as location and direction information as provided by the mobile(More)