The increasing presence of image/video capture devices such as camera phones and surveillance cameras has become a ubiquitous element of providing convenience and improving security in modern life. On the other hand, the pervasiveness of such image/video capture devices raises growing privacy concerns. In this paper, we concentrate on a new visual privacy protection problem—covert photo classification. Covert photography means that the subject being photographed is purposely made unaware that he or she is photographed. A covert photo often contains information that is inherently sensitive and private to a person. If such photos are released on the public without approval, it may lead to serious negative consequences. We explore deep convolutional neural networks (DCNNs) to discover intricate structures of covert photos and automatically learn the representations for covert photo classification. Experimental results demonstrate that DCNN-based architectures which are fully end-to-end trained reach beyond previous experience-dependent hand-engineered feature methods in covert photo classification. The fusion of three DCNN-based architectures (AlexNet, VGGS, and GoogleNet) shows enhanced performance over individual networks on the Covert-2500 dataset and achieves an average classification rate (1-EER) of 0.925 which significantly outperforms the result (1-EER) of 0.8940 of hand-engineered feature methods.
Machine Vision and Applications – Springer Journals
Published: Jul 17, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera