(untitled) (International Journal of Engineering and Computer Science, 2017)
The advances in image acquisition technique made recording images never easier and brings a great convenience 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 and often violate the subject willingness. Covert photos are often privacy invasive and, if distributed over Internet, can cause serious consequences. Automatic identification of such photos, therefore, serves as an important initial step toward further privacy protection operations. The problem is, however, very challenging due to the large semantic similarity between covert and noncovert photos, the enormous diversity in the photographing process and environment of cover photos, and the difficulty to collect an effective data set for the study. To overcome these challenges three contributions are used. First is to consider a dataset of 2500 covert images and verify each image carefully. Second is to conduct user study on how human perform to distinguish between covert and non-covert images. Third is to perform covert photo classification algorithm that fuses various image features and visual attributes in the multiple kernel learning framework.