Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201

Homosecoiridoid Alkaloids with Amino Acid Units from the Flower Buds of <i>Lonicera japonica</i>

Nine new homosecoiridoid alkaloids, named lonijaposides O–W (<b>1</b>–<b>9</b>), along with 19 known compounds, were isolated from an aqueous extract of the flower buds of <i>Lonicera japonica</i>. Their structures and absolute configurations were determined by spectroscopic data analysis and chemical methods. Lonijaposides O–W have structural features that involve amino acid units sharing the N atom with a pyridinium (<b>1</b>–<b>5</b>) or nicotinic acid (<b>6</b>–<b>9</b>) moiety. The absolute configurations of the amino acid units were determined by oxidation of each pyridinium ring moiety with potassium ferricyanide, hydrolysis of the oxidation product, and Marfey’s analysis of the hydrolysate. This procedure was validated by oxidizing and hydrolyzing synthetic model compounds. The phenylalanine units in compounds <b>4</b>, <b>5</b>, and <b>9</b> have the d-configuration, and the other amino acid units in <b>1</b>–<b>3</b> and <b>6</b>–<b>8</b> possess the l-configuration. Compounds <b>1</b>, <b>4</b>, <b>6</b>, and <b>9</b> and the known compounds 3,4-di-<i>O</i>-caffeoylquinic acid, 3,5-di-<i>O</i>-caffeoylquinic acid, and 5′-<i>O</i>-methyladenosine exhibited antiviral activity against the influenza virus A/Hanfang/359/95 (H3N2) with IC₅₀ values of 3.4–11.6 μM, and <b>4</b> inhibited Coxsackie virus B3 replication with an IC₅₀ value of 12.3 μM