Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Preventing Accidental Privacy Leakage in Ubiquitous Visual Sensing

The digital cameras are ubiquitous in our daily life and became an essential part of everyday devices. For example, there are cameras in cellphones, tablets, and many other wearable devices. This facilitates capturing and storing information around us through taking images and videos. Despite of the easiness and usefulness of cameras, they also bring a lot of serious privacy problems. These privacy problems can be either malicious intent or accidental user own ignorance, for example, malicious secret filming or unwanted recognition from a photo. To illustrate, the camera cannot distinguish whether part of the data captured is private or sensitive. Once the picture is exposed, accidental privacy leakage may happen. In many cases, we cannot avoid the malicious picture capture. However, we can avoid the information leakage in proactive way. Our goal in this thesis work is to prevent or minimize accidental privacy leakage. To achieve it, the basic idea is to mark the sensitive areas or objects by QR code, in order to encode the privacy information in such targets. Thus those areas and objects can be filtered out before publishing. Usually, face is a common case as the sensitive information, which we used in this thesis. In our implementation, the recognized faces were blurred from the original picture before publishing. We discussed the implementation of proactive protection of information from three aspects, including real time none-object based approach, offline none-object based approach and object based approach. In the real-time approach, QR code is used to implement real time processing. By using this feasible method, user can have fine grained control on what is revealed and what is kept private. In the offline method, a range selector is implemented to eliminate the specific area, even though there are still some defects of this approach. For example, it is not real time, and the lists of vii sensitive area differs from picture to picture. To help our implementation, this research utilized face recognition for the object based method as well. Compared with none object based sensing, this approach has less feasibility, lower speed, and less accuracy. The thesis categorizes existed ways by two types (permission requirement and predefine sensitive area) and compares these ways from different perspectives through showing the pros and cons for each side.Master of ScienceComputer and Information Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/136612/1/YingZou_thesis.pdfDescription of YingZou_thesis.pdf : Thesi

Information Forensics and Security: A quarter-century-long journey

Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends

Automated Framework to Improve User?s Awareness and to Categorize Friends on Online Social Networks

The popularity of online social networks has brought up new privacy threats. These threats often arise after users willingly, but unwittingly reveal their information to a wider group of people than they actually intended. Moreover, the well adapted ?friends-based? privacy control has proven to be ill-equipped to prevent dynamic information disclosure, such as in user text posts. Ironically, it fails to capture the dynamic nature of this data by reducing the problem to manual privacy management which is time-consuming, tiresome and error-prone task. This dissertation identifies an important problem with posting on social networks and proposes a unique two phase approach to the problem. First, we suggest an additional layer of security be added to social networking sites. This layer includes a framework for natural language to automatically check texts to be posted by the user and detect dangerous information disclosure so it warns the user. A set of detection rules have been developed for this purpose and tested with over 16,000 Facebook posts to confirm the detection quality. The results showed that our approach has an 85% detection rate which outperforms other existing approaches. Second, we propose utilizing trust between friends as currency to access dangerous posts. The unique feature of our approach is that the trust value is related to the absence of interaction on the given topic. To approach our goal, we defined trust metrics that can be used to determine trustworthy friends in terms of the given topic. In addition, we built a tool which calculates the metrics automatically, and then generates a list of trusted friends. Our experiments show that our approach has reasonably acceptable performance in terms of predicting friends? interactions for the given posts. Finally, we performed some data analysis on a small set of user interaction records on Facebook to show that friends? interaction could be triggered by certain topics

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Enhancing Online Security with Image-based Captchas

Given the data loss, productivity, and financial risks posed by security breaches, there is a great need to protect online systems from automated attacks. Completely Automated Public Turing Tests to Tell Computers and Humans Apart, known as CAPTCHAs, are commonly used as one layer in providing online security. These tests are intended to be easily solvable by legitimate human users while being challenging for automated attackers to successfully complete. Traditionally, CAPTCHAs have asked users to perform tasks based on text recognition or categorization of discrete images to prove whether or not they are legitimate human users. Over time, the efficacy of these CAPTCHAs has been eroded by improved optical character recognition, image classification, and machine learning techniques that can accurately solve many CAPTCHAs at rates approaching those of humans. These CAPTCHAs can also be difficult to complete using the touch-based input methods found on widely used tablets and smartphones.;This research proposes the design of CAPTCHAs that address the shortcomings of existing implementations. These CAPTCHAs require users to perform different image-based tasks including face detection, face recognition, multimodal biometrics recognition, and object recognition to prove they are human. These are tasks that humans excel at but which remain difficult for computers to complete successfully. They can also be readily performed using click- or touch-based input methods, facilitating their use on both traditional computers and mobile devices.;Several strategies are utilized by the CAPTCHAs developed in this research to enable high human success rates while ensuring negligible automated attack success rates. One such technique, used by fgCAPTCHA, employs image quality metrics and face detection algorithms to calculate a fitness value representing the simulated performance of human users and automated attackers, respectively, at solving each generated CAPTCHA image. A genetic learning algorithm uses these fitness values to determine customized generation parameters for each CAPTCHA image. Other approaches, including gradient descent learning, artificial immune systems, and multi-stage performance-based filtering processes, are also proposed in this research to optimize the generated CAPTCHA images.;An extensive RESTful web service-based evaluation platform was developed to facilitate the testing and analysis of the CAPTCHAs developed in this research. Users recorded over 180,000 attempts at solving these CAPTCHAs using a variety of devices. The results show the designs created in this research offer high human success rates, up to 94.6\% in the case of aiCAPTCHA, while ensuring resilience against automated attacks

Mothers\u27 Adaptation to Caring for a New Baby

To date, most research on parents\u27 adjustment after adding a new baby to their family unit has focused on mothers\u27 initial transition to parenthood. This past research has examined changes in mothers\u27 marital satisfaction and perceived well-being across the transition, and has compared their prenatal expectations to their postnatal experiences. This project assessed first-time and experienced mothers\u27 stress and satisfaction associated with parenting, their adjustment to competing demands, and their perceived well-being longitudinally before and after the birth of a baby. Additionally, how maternal and child-related variables influenced the trajectory of mothers\u27 postnatal adaptation was assessed. These variables included mothers\u27 age, their education level, their prenatal expectations and postnatal experiences concerning shared infant care, their satisfaction with the division of infant caregiving, and their perceptions of their infant\u27s temperament. Mothers (N = 136) completed an online survey during their third trimester and additional online surveys when their baby was approximately 2, 4, 6, and 8 weeks old.;First-time mothers prenatally expected a more equal division of infant caregiving between themselves and their partners than did experienced mothers. Both first-time and experienced mothers reported less assistance from their partners than they had prenatally expected. Additionally, they experienced almost twice as many violated expectations than met expectations. Growth curve modeling revealed that a cubic function of time best fit the trajectory of mothers\u27 postnatal parenting satisfaction. Mothers reported less parenting satisfaction at 4 weeks, compared to 2 and 6 weeks, and reported stability in their satisfaction between 6 and 8 weeks. A quadratic function of time best fit the trajectories of mothers\u27 postnatal parenting stress and adjustment to the demands of their baby. Mothers reported more stress and difficulty adjusting to their baby\u27s demands at 4 and 6 weeks, compared to 2 and 8 weeks. A linear function of time best fit the trajectories of mothers\u27 adjustment to home demands, generalized state anxiety, and depressive symptoms. Mothers reported less difficulty meeting home demands, less generalized anxiety, and fewer depressive symptoms across the postnatal period. Mothers\u27 violated expectations were associated with level differences in all aspects of mothers\u27 postnatal adaptation except their adjustment to home demands. Specifically, more violated expectations, in number or in magnitude, were associated with poorer postnatal adaptation. Mothers\u27 violated expectations were not associated with the slope of mothers\u27 postnatal adaptation trajectories. Exploratory models revealed that other maternal and child-related variables also impacted the level and slope of mothers\u27 postnatal adaptation.;Overall, first-time and experienced mothers were more similar than different in regards to their postnatal adaptation. This study suggests that prior findings concerning adults\u27 initial transition to parenthood may also apply to adults during each addition of a new baby into the family unit. Additionally, mothers who reported less of a mismatch between their expectations and experiences concerning shared infant care had fewer issues adapting the postnatal period. Thus, methods to increase the assistance mothers receive from their partner should be sought. Limitations of this study and suggestions for future research are also discussed