Towards Face Encryption by Generating Adversarial Identity Masks

Yang, Xiao; Dong, Yinpeng; Pang, Tianyu; Su, Hang; Zhu, Jun; Chen, Yuefeng; Xue, Hui

doi:10.1109/iccv48922.2021.00387

Cited by 51 publications

(54 citation statements)

References 36 publications

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“…[987] use adversarial training methods to improve machine reading comprehension. [988] exploit adversarial attacks to generating adversarial identity masks, in order to protect user privacy. Adversarially robust models also have more semantic input gradients, which can connect with generative learning methods like score matching [989,990,991,992] and SGLD [993,994].…”

Section: Adversarial For Goodmentioning

confidence: 99%

A Roadmap for Big Model

Yuan¹,

Zhao²,

Jiahong³

et al. 2022

Preprint

Self Cite

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domains indexed by Google News. It contains 31 million documents with an average length of 793 BPE tokens. Like C4, it excludes examples with duplicate URLs. News dumps from December 2016 through March 2019 were used as training data, articles published in April 2019 from the April 2019 dump were used for evaluation. OpenWebText2(OWT2). OWT2 is an enhanced version of the original OpenWebTextCorpus, including content from multiple languages, document metadata, multiple dataset versions, and open source replication code, covering all Reddit submissions from 2005 up until April 2020. PubMed Central(PMC). PMC is a free full-text archive of biomedical and life sciences journal literature from the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The dataset is updated daily. In addition to full-text articles, they contain corrections, retractions, and expressions of concern, as well as file lists that include metadata for articles in each dataset.PMC obtained by open registration in Amazon Web Services (AWS) includes The PMC Open Access Subset and The Author Manuscript Dataset. The PMC Open Access Subset includes all articles and preprints in PMC with a machine-readable Creative Commons license that allows reuse. The Author Manuscript Dataset includes accepted author manuscripts collected under a funder policy in PMC and made available in machine-readable formats for text mining. ArXiv. ArXiv is a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more. It provides open access to academic articles, covering many subdisciplines from vast branches of physics to computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics, which is helpful to the potential downstream applications of the research field. In addition, the writing language of LaTeX also contributes to the study of language models. Colossal Clean Crawled Corpus(C4). C4 is a colossal, cleaned version of Common Crawl's web crawl corpus. It is based on Common Crawl dataset and was used to train the T5 text-to-text Transformer models. The cleaned English version of C4 has 364,868,901 training examples and 364,608 validation examples, while the uncleaned English version has 1,063,805,324 training examples and 1,065,029 validation examples; the realnewslike version has 13,799,838 training examples and 13,863 validation examples, while the webtextlike version has 4,500,788 training examples and 4,493 validation examples. Wiki-40B. Wikipedia (Wiki-40B) is a clean-up text collection containing more than 40 Wikipedia language editions of pages corresponding to entities. The dataset is split into train/validation/test sets for each language. The training set has 2,926,536 examples, the validation set has 163,597 examples, and the test set has 162,274 examples. Wiki-40B is cleaned by a page filter to remove ambiguous, redirected, deleted, and non-physical pages. CLUECorpus2020. CLUECorpus2020 ...

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Section: Adversarial For Goodmentioning

confidence: 99%

A Roadmap for Big Model

Yuan¹,

Zhao²,

Jiahong³

et al. 2022

Preprint

Self Cite

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“…More recent proposals propose methods designed to be more robust to real-world FR systems (i.e. joint optimization on multiple feature extractors, etc) [27], [23], [114]. Another recent proposal [111] uses a GAN to generate adversarial perturbations rather than applying the above mentioned optimization techniques.…”

Section: Digital Evasion Techniquesmentioning

confidence: 99%

SoK: Anti-Facial Recognition Technology

Wenger¹,

Shan²,

Zheng³

et al. 2021

Preprint

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The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition. The set of AFR tools proposed in the last few years is wide-ranging and rapidly evolving, necessitating a step back to consider the broader design space of AFR systems and long-term challenges. This paper aims to fill that gap and provides the first comprehensive analysis of the AFR research landscape. Using the operational stages of FR systems as a starting point, we create a systematic framework for analyzing the benefits and tradeoffs of different AFR approaches. We then consider both technical and social challenges facing AFR tools and propose directions for future research in this field.

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“…To protect the privacy when using face images, recent studies have exploited two major approaches: facial attributes editing [4][5][6][7] and de-identification [8][9][10][11][12][13]. However, altering * corresponding author facial attributes results in the loss of original information (e.g., facial expressions) for facial analysis applications (e.g., psychological diagnosis).…”

Section: Introductionmentioning

confidence: 99%

“…However, altering * corresponding author facial attributes results in the loss of original information (e.g., facial expressions) for facial analysis applications (e.g., psychological diagnosis). Meanwhile, most existing deidentification studies (e.g., [9][10][11][12][13]) only demonstrated the deidentification performance with producing realistic face images, but merely quantitatively evaluate whether the facial attributes in the original images are preserved. Those facial attributes are critical for facial analysis, which should be preserved while de-identifying face images.…”

Section: Introductionmentioning

confidence: 99%

“…There are several related works in the 2D domain. These studies include face swapping [19], region of interest editing [20], template morphing [21], adding mosaic [22], while the adversarial training approach [8][9][10][11][12][13] has attracted the most attention. Apart from those focusing on face deidentification, other related research focuses more on facial attributes such as gender [6], age and ethnicity [7], and facial expression [8,[23][24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking 3D Face De-Identification with Preserving Facial Attributes

Cheng

Chen

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Privacy with the use of face images is becoming a major concern in civilians' applications. Recent studies have exploited privacy protection methods by means of facial attributes editing or de-identifying face images. Altering attributes causes loss of information for facial analysis while most de-identification studies did not quantitatively evaluate how well facial attributes are preserved. Moreover, state-ofthe-art face analysis utilized 3D information for better performance. Existing face privacy studies only focusing in 2D domain is a key limitation towards the compatibility of more advanced 3D face analysis. This paper presents the first study on the possibility of 3D face de-identification with preserving facial attributes. We systematically evaluate the performance of 2D/3D face/facial attribute recognition and develop 2D/3D de-identification methods with preserving facial attributes using Auto Encoder and Generative Adversarial Networks approaches. We present comprehensive and reproducible experimental results using a publicly available 3D face database with facial attribute annotations for benchmarking and further research. https://github.com/kevinhmcheng/3d-face-de-id

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Towards Face Encryption by Generating Adversarial Identity Masks

Cited by 51 publications

References 36 publications

A Roadmap for Big Model

A Roadmap for Big Model

SoK: Anti-Facial Recognition Technology

Benchmarking 3D Face De-Identification with Preserving Facial Attributes

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