A Survey on Covert photo Classification. (International Journal of Engineering and Computer Science, 2017)
The advances in image acquisition technique made recording images never easier and brings a greatconvenience to our daily life. It raises at the same time the issue of privacy protection in the photographs.One particular problem addressed in this paper is about covert photographs, which are taken secretly andoften violate the subject willingness. Covert photos are often privacy invasive and, if distributed overInternet, can cause serious consequences. Automatic identification of such photos, therefore, serves as animportant initial step toward further privacy protection operations. The problem is, however, verychallenging due to the large semantic similarity between covert and noncovert photos, the enormousdiversity in the photographing process and environment of cover photos, and the difficulty to collect aneffective data set for the study. To overcome these challenges three contributions are used. First is toconsider a dataset of 2500 covert images and verify each image carefully. Second is to conduct user studyon how human perform to distinguish between covert and non-covert images. Third is to perform covertphoto classification algorithm that fuses various image features and visual attributes in the multiple kernellearning framework.