Different Approaches to Blurring Digital Images and Their Effect on Facial Detection

The purpose of this thesis is to analyze the usage of multiple image blurring techniques and determine their effectiveness in combatting facial detection algorithms. This type of analysis is anticipated to reveal potential flaws in the privacy expected from blurring images or, rather, portions of images. Three different blurring algorithms were designed and implemented: a box blurring method, a Gaussian blurring method, and a differential privacy-based pixilation method. Datasets of images were collected from multiple sources, including the AT&T Database of Faces. Each of these three methods were implemented via their own original method, but, because of how common they are, box blurring and Gaussian blurring were also implemented utilizing the OpenCV open-source library to conserve time. Extensive tests were run on each of these algorithms, including how the blurring acts on color and grayscale images, images with and without faces, and the effectiveness of each blurring algorithm in hiding faces from being detected via the popular open-source OpenCV library facial detection method. Of the chosen blurring techniques, the differential privacy blurring method appeared the most effective against mitigating facial detection

On the Convergence and Consistency of the Blurring Mean-Shift Process

The mean-shift algorithm is a popular algorithm in computer vision and image processing. It can also be cast as a minimum gamma-divergence estimation. In this paper we focus on the "blurring" mean shift algorithm, which is one version of the mean-shift process that successively blurs the dataset. The analysis of the blurring mean-shift is relatively more complicated compared to the nonblurring version, yet the algorithm convergence and the estimation consistency have not been well studied in the literature. In this paper we prove both the convergence and the consistency of the blurring mean-shift. We also perform simulation studies to compare the efficiency of the blurring and the nonblurring versions of the mean-shift algorithms. Our results show that the blurring mean-shift has more efficiency.Comment: arXiv admin note: text overlap with arXiv:1201.197