VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People

Gurari, Danna; Li, Qing; Lin, Chao; Zhao, Yinan; Guo, Anhong; Stangl, Abigale; Bigham, Jeffrey P.

doi:10.1109/cvpr.2019.00103

Cited by 58 publications

(44 citation statements)

References 36 publications

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“…Existing datasets collected for studying problems in the area of visual privacy, such as PicAlert [275], YourAlert [276], Campus Face Set [277] VISPR [171], Visual Redactions [35], or VizWiz-Priv [278], (in a significant part) also include facial images, but typically aim to address privacy concerns associated with visual data that go beyond biometric privacy enhancement. As a result, only limited work related to B-PETs has been done with these datasets so far.…”

Section: Dedicated Datasetsmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

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Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy-related research in the area of biometrics and review existing work on Biometric Privacy-Enhancing Techniques (B-PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B-PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

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Section: Dedicated Datasetsmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

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“…VizWiz dataset [5,6] is published in 2018 and updated until now. It was the first dataset that mentioned the Answerability problem in Visual Question Answering.…”

Section: Vizwiz Datasetmentioning

confidence: 99%

“…In this work, we focus on an attractive and novel task, Answerability on Visual Question Answering (VQA), which is recently proposed in a contest of VQA for blind people [5,6] in 2018. Generally, Answerability is to determine a score that reflects the answerable ability of samples.…”

Section: Introductionmentioning

confidence: 99%

Vision And Text Transformer For Predicting Answerability On Visual Question Answering

Nguyen

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Answerability on Visual Question Answering is a novel and attractive task to predict answerable scores between images and questions in multi-modal data. Existing works often utilize a binary mapping from visual question answering systems into Answerability. It does not reflect the essence of this problem. Together with our consideration of Answerability in a regression task, we propose VT-Transformer, which exploits visual and textual features through Transformer architecture. Experimental results on VizWiz 2020 dataset show the effectiveness and robustness of VT-Transformer for Answerability on Visual Question Answering when comparing with competitive baselines.

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“…Previous work studying user privacy on SMNs have focused on multiparty privacy conflicts [5,6], images or text content from users [1,[7][8][9][10][11][12][13][14], third-party applications that supervise privacy [11,[13][14][15], cultures in university settings/communities [1], the users' SMNs privacy settings [1,3,16,17], studies on users' attitudes, intention, or behaviors [7,17], and children/teenagers' interactions with SMNs [18,19].…”

Section: Previous Literaturementioning

confidence: 99%

Social Media and the Scourge of Visual Privacy

2020

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Online privacy has become immensely important with the growth of technology and the expansion of communication. Social Media Networks have risen to the forefront of current communication trends. With the current trends in social media, the question now becomes how can we actively protect ourselves on these platforms? Users of social media networks share billions of images a day. Whether intentional or unintentional, users tend to share private information within these images. In this study, we investigate (1) the users’ perspective of privacy, (2) pervasiveness of privacy leaks on Twitter, and (3) the threats and dangers on these platforms. In this study, we incorporate techniques such as text analysis, analysis of variance, and crowdsourcing to process the data received from these sources. Based on the results, the participants’ definitions of privacy showed overlap regardless of age or gender identity. After looking at the survey results, most female participants displayed a heightened fear of dangers on social media networks because of threats in the following areas: assets and identity. When the participants were asked to rank the threats on social media, they showed a high concern for burglary and kidnapping. We find that participants need more education about the threats of visual content and how these privacy leaks can lead to physical, mental, and emotional danger.

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VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People

Cited by 58 publications

References 36 publications

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Vision And Text Transformer For Predicting Answerability On Visual Question Answering

Social Media and the Scourge of Visual Privacy

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