We describe an online system for classifying computer generated images and camera-captured photographic images, as part of our effort in building a complete passive-blind system for image tampering detection (project website at http: //www.ee.columbia.edu/trustfoto). Users are able to submit any image from a local or an online source to the system and get classification results with confidence scores. Our system has implemented three different algorithms from the state of the art based on the geometry, the wavelet, and the cartoon features. We describe the important algorithmic issues involved for achieving satisfactory performances in both speed and accuracy as well as the capability to handle diverse types of input images. We studied the effects of image size reduction on classification accuracy and speed, and found different size reduction methods worked best for different classification methods. In addition, we incorporated machine learning techniques, such as fusion and subclass-based bagging, in order to counter the effect of performance degradation caused by image size reduction. With all these improvements, we are able to speed up the classification speed by more than two times while keeping the classification accuracy almost intact at about 82%.
Recently we have witnessed a growing interest in the development of the subband/wavelet coding (SBC) technology, partly due to the superior scalability of SBC. Scalable coding provides great synergy with the universal media access applications, where media content is delivered to client devices of diverse types through heterogeneous channels. In this respect, SBC system provides flexibility in realizing different ways of media scaling, including scaling dimensions of SNR, spatial, and temporal. However, the selection of specific scalability operations given the bit rate constraint has always been ad hoc - a systematic methodology is missing. In this paper, we address this open issue by applying our content-based optimal scalability selection framework and adopting subjective quality evaluation. For this purpose we firstly explore the behavior of SNR-Spatial-Temporal scalability using Motion Compensated (MC) SBC systems. Based on the system behavior, we propose an efficient method for the optimal selection of scalability operator through content-based prediction. Our experiment results demonstrate that the proposed method can efficiently predict the optimal scalability operation with an excellent accuracy.