Conceivable security risks and authentication techniques for smart devices

With the rapidly escalating use of smart devices and fraudulent transaction of users’ data from their devices, efficient and reliable techniques for authentication of the smart devices have become an obligatory issue. This paper reviews the security risks for mobile devices and studies several authentication techniques available for smart devices. The results from field studies enable a comparative evaluation of user-preferred authentication mechanisms and their opinions about reliability, biometric authentication and visual authentication techniques

AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

Reversible de-identification for lossless image compression using reversible watermarking

De-Identification is a process which can be used to ensure privacy by concealing the identity of individuals captured by video surveillance systems. One important challenge is to make the obfuscation process reversible so that the original image/video can be recovered by persons in possession of the right security credentials. This work presents a novel Reversible De-Identification method that can be used in conjunction with any obfuscation process. The residual information needed to reverse the obfuscation process is compressed, authenticated, encrypted and embedded within the obfuscated image using a two-level Reversible Watermarking scheme. The proposed method ensures an overall single-pass embedding capacity of 1.25 bpp, where 99.8% of the images considered required less than 0.8 bpp while none of them required more than 1.1 bpp. Experimental results further demonstrate that the proposed method managed to recover and authenticate all images considered.peer-reviewe

Iris Recognition Approach for Preserving Privacy in Cloud Computing

Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy

Automated Privacy Protection for Mobile Device Users and Bystanders in Public Spaces

As smartphones have gained popularity over recent years, they have provided usersconvenient access to services and integrated sensors that were previously only available through larger, stationary computing devices. This trend of ubiquitous, mobile devices provides unparalleled convenience and productivity for users who wish to perform everyday actions such as taking photos, participating in social media, reading emails, or checking online banking transactions. However, the increasing use of mobile devices in public spaces by users has negative implications for their own privacy and, in some cases, that of bystanders around them. Specifically, digital photography trends in public have negative implications for bystanders who can be captured inadvertently in users’ photos. Those who are captured often have no knowledge of being photographed and have no control over how photos of them are distributed. To address this growing issue, a novel system is proposed for protecting the privacy of bystanders captured in public photos. A fully automated approach to accurately distinguish the intended subjects from strangers is explored. A feature-based classification scheme utilizing entire photos is presented. Additionally, the privacy-minded case of only utilizing local face images with no contextual information from the original image is explored with a convolutional neural network-based classifier. Three methods of face anonymization are implemented and compared: black boxing, Gaussian blurring, and pose-tolerant face swapping. To validate these methods, a comprehensive user survey is conducted to understand the difference in viability between them. Beyond photographing, the privacy of mobile device users can sometimes be impacted in public spaces, as visual eavesdropping or “shoulder surfing” attacks on device screens become feasible. Malicious individuals can easily glean personal data from smartphone and mobile device screens while they are accessed visually. In order to protect displayed user content, anovel, sensor-based visual eavesdropping detection scheme using integrated device cameras is proposed. In order to selectively obfuscate private content while an attacker is nearby, a dynamic scheme for detecting and hiding private content is also developed utilizing User-Interface-as-an-Image (UIaaI). A deep, convolutional object detection network is trained and utilized to identify sensitive content under this scheme. To allow users to customize the types ofcontent to hide, dynamic training sample generation is introduced to retrain the content detection network with very few original UI samples. Web applications are also considered with a Chrome browser extension which automates the detection and obfuscation of sensitive web page fields through HTML parsing and CSS injection

DIGITAL INPAINTING ALGORITHMS AND EVALUATION

Digital inpainting is the technique of filling in the missing regions of an image or a video using information from surrounding area. This technique has found widespread use in applications such as restoration, error recovery, multimedia editing, and video privacy protection. This dissertation addresses three significant challenges associated with the existing and emerging inpainting algorithms and applications. The three key areas of impact are 1) Structure completion for image inpainting algorithms, 2) Fast and efficient object based video inpainting framework and 3) Perceptual evaluation of large area image inpainting algorithms. One of the main approach of existing image inpainting algorithms in completing the missing information is to follow a two stage process. A structure completion step, to complete the boundaries of regions in the hole area, followed by texture completion process using advanced texture synthesis methods. While the texture synthesis stage is important, it can be argued that structure completion aspect is a vital component in improving the perceptual image inpainting quality. To this end, we introduce a global structure completion algorithm for completion of missing boundaries using symmetry as the key feature. While existing methods for symmetry completion require a-priori information, our method takes a non-parametric approach by utilizing the invariant nature of curvature to complete missing boundaries. Turning our attention from image to video inpainting, we readily observe that existing video inpainting techniques have evolved as an extension of image inpainting techniques. As a result, they suffer from various shortcoming including, among others, inability to handle large missing spatio-temporal regions, significantly slow execution time making it impractical for interactive use and presence of temporal and spatial artifacts. To address these major challenges, we propose a fundamentally different method based on object based framework for improving the performance of video inpainting algorithms. We introduce a modular inpainting scheme in which we first segment the video into constituent objects by using acquired background models followed by inpainting of static background regions and dynamic foreground regions. For static background region inpainting, we use a simple background replacement and occasional image inpainting. To inpaint dynamic moving foreground regions, we introduce a novel sliding-window based dissimilarity measure in a dynamic programming framework. This technique can effectively inpaint large regions of occlusions, inpaint objects that are completely missing for several frames, change in size and pose and has minimal blurring and motion artifacts. Finally we direct our focus on experimental studies related to perceptual quality evaluation of large area image inpainting algorithms. The perceptual quality of large area inpainting technique is inherently a subjective process and yet no previous research has been carried out by taking the subjective nature of the Human Visual System (HVS). We perform subjective experiments using eye-tracking device involving 24 subjects to analyze the effect of inpainting on human gaze. We experimentally show that the presence of inpainting artifacts directly impacts the gaze of an unbiased observer and this in effect has a direct bearing on the subjective rating of the observer. Specifically, we show that the gaze energy in the hole regions of an inpainted image show marked deviations from normal behavior when the inpainting artifacts are readily apparent

Biometric Privacy Protection based on Combination of Hiding and Chaotic Encryption

 نتيجة لاستخدام أنظمة القياسات الحيوية بكثرة، أصبحت سلامة ميزة القياسات الحيوية ذات أهمية كبيرة. عندما يتم نقل الصور البيومترية عبر قنوات غير آمنة أو تخزينها كبيانات أولية ، فإنها تصبح عرضة لخطر السرقة والتزوير والهجوم. يعد إخفاء البيانات أحد الأساليب الرئيسية لحماية الخصوصية. الهدف من إخفاء البيانات البيومترية هو تضمين البيانات الشخصية ززفي غلاف القياسات الحيوية والحفاظ على أداء التعرف. تقدم الفكرة الورقية مستويين من الأمان يعتمدان على الإخفاء والتشفير. يتم تجزأت صورة العين إلى جزئيين او منطقتين هما  (ROI)  (NROI) وبقصد بهما منطقة مهمة ومنطقة غير مهمة .يتم تضمين بيانات الخصوصية مع NROI ثم إعادة تجميع الصورة باستخدام ROI  للقزحية للحصول على صورة مدمجة. ثم يتم تطبيق التشفير العشوائي على الصورة المضمنة للحصول على مستوى عالٍ من الأمان. تم اختبار النتائج التجريبية باستخدام مجموعة بيانات CASIA1.تم استخدام مقياسيين هما PSNR و NC. أظهرت نتائج الاختبار قيمة عالية لل  PSNR مما يعني احتفاظ صورة الغلاف بجودتها وقيمة NC هي (1) مما يعني استرجاع مثالي للبيانات السرية. كما وتم اختبار طريقة التشفير باستخدام قياسات مثل الرسم البياني والارتباط والنتروبيا وجميع النتائج كانت جيدة.With the expanded use of biometric systems, the safety of the biometric feature has become increasingly important. When biometric images are transferred through unsafe channels or stored as raw data, they become at risk of theft, forgery and attack. Data hiding is one of the main techniques of Privacy Protection. The goal of biometric data hiding is for adequate personal data is to be included in the cover of Biometrics and to maintain recognition performance. The paper idea introduces two levels of security based on hiding and encryption. The eye image is segmented into two regions Region of Interest (ROI) and Non-Region of Interest (NROI), The iris segmentation method depends on the Circular Hough Transform (CHT). The privacy data is embedded with NROI and then reassemble the image with ROI (iris) to get the embedding image. Then chaotic encryption is applied on the embedded image to get a high level of security. The experimental results are tested using the CASIA1 data set. The tests of hiding level are done using measurements such as PSNR and NC. The results show that the suggested method gives a higher value of PSNR which means not destroy the cover image and the value of NC is (1) which means a perfect reconstruction of secret data. The tests on encryption levels show good results using measurements such as histogram, correlation, and entropy