diff options
Diffstat (limited to 'site/content')
| -rw-r--r-- | site/content/pages/about/attribution.md | 2 | ||||
| -rw-r--r-- | site/content/pages/about/index.md | 13 | ||||
| -rw-r--r-- | site/content/pages/datasets/brainwash/index.md | 14 | ||||
| -rw-r--r-- | site/content/pages/datasets/duke_mtmc/index.md | 4 | ||||
| -rw-r--r-- | site/content/pages/datasets/msceleb/index.md | 85 | ||||
| -rw-r--r-- | site/content/pages/datasets/oxford_town_centre/index.md | 6 | ||||
| -rw-r--r-- | site/content/pages/datasets/uccs/index.md | 4 | ||||
| -rw-r--r-- | site/content/pages/research/02_what_computers_can_see/index.md | 7 |
8 files changed, 104 insertions, 31 deletions
diff --git a/site/content/pages/about/attribution.md b/site/content/pages/about/attribution.md index cf537ad4..5060b2d9 100644 --- a/site/content/pages/about/attribution.md +++ b/site/content/pages/about/attribution.md @@ -32,7 +32,7 @@ If you use the MegaPixels data or any data derived from it, please cite the orig title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, year = 2019, url = {https://megapixels.cc/}, - urldate = {2019-04-20} + urldate = {2019-04-18} } </pre> diff --git a/site/content/pages/about/index.md b/site/content/pages/about/index.md index f68008cc..a6ce3d3d 100644 --- a/site/content/pages/about/index.md +++ b/site/content/pages/about/index.md @@ -24,8 +24,9 @@ authors: Adam Harvey MegaPixels is an independent art and research project by Adam Harvey and Jules LaPlace that investigates the ethics, origins, and individual privacy implications of face recognition image datasets and their role in the expansion of biometric surveillance technologies. +MegaPixels is made possible with support from <a href="http://mozilla.org">Mozilla</a>, our primary funding partner. -The MegaPixels site is made possible with support from <a href="http://mozilla.org">Mozilla</a> +Additional support for MegaPixels is provided by the European ARTificial Intelligence Network (AI LAB) at the Ars Electronica Center, 1-year research-in-residence grant from Karlsruhe HfG, and sales from the Privacy Gift Shop. <div class="flex-container team-photos-container"> @@ -85,6 +86,16 @@ You are free: Please direct questions, comments, or feedback to [mastodon.social/@adamhrv](https://mastodon.social/@adamhrv) +#### Funding Partners + +The MegaPixels website, research, and development is made possible with support form Mozilla, our primary funding partner. + +[ add logos ] + +Additional support is provided by the European ARTificial Intelligence Network (AI LAB) at the Ars Electronica Center and a 1-year research-in-residence grant from Karlsruhe HfG. + +[ add logos ] + ##### Attribution If you use MegaPixels or any data derived from it for your work, please cite our original work as follows: diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index b57bcdf4..75b0c006 100644 --- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -8,8 +8,8 @@ slug: brainwash cssclass: dataset image: assets/background.jpg year: 2015 -published: 2019-2-23 -updated: 2019-2-23 +published: 2019-4-18 +updated: 2019-4-18 authors: Adam Harvey ------------ @@ -25,24 +25,20 @@ The Brainwash dataset is unique because it uses images from a publicly available Although Brainwash appears to be a less popular dataset, it was used in 2016 and 2017 by researchers from the National University of Defense Technology in China took note of the dataset and used it for two [research](https://www.semanticscholar.org/paper/Localized-region-context-and-object-feature-fusion-Li-Dou/b02d31c640b0a31fb18c4f170d841d8e21ffb66c) [projects](https://www.semanticscholar.org/paper/A-Replacement-Algorithm-of-Non-Maximum-Suppression-Zhao-Wang/591a4bfa6380c9fcd5f3ae690e3ac5c09b7bf37b) on advancing the capabilities of object detection to more accurately isolate the target region in an image ([PDF](https://www.itm-conferences.org/articles/itmconf/pdf/2017/04/itmconf_ita2017_05006.pdf)). [^localized_region_context] [^replacement_algorithm]. The dataset also appears in a 2017 [research paper](https://ieeexplore.ieee.org/document/7877809) from Peking University for the purpose of improving surveillance capabilities for "people detection in the crowded scenes". - - + {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} + - - - - + {% include 'cite_our_work.html' %} ### Footnotes - [^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. [^end_to_end]: Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. [^localized_region_context]: Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598. diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index 0f4986de..2420d042 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -7,8 +7,8 @@ subdesc: Duke MTMC contains over 2 million video frames and 2,700 unique identit slug: duke_mtmc cssclass: dataset image: assets/background.jpg -published: 2019-2-23 -updated: 2019-2-23 +published: 2019-4-18 +updated: 2019-4-18 authors: Adam Harvey ------------ diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md index d5e52952..0c78e094 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -2,14 +2,14 @@ status: published title: Microsoft Celeb -desc: MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms -subdesc: The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine +desc: Microsoft Celeb 1M is a target list and dataset of web images used for research and development of face recognition technologies +subdesc: The MS Celeb dataset includes over 10 million images of about 100K people and a target list of 1 million individuals slug: msceleb cssclass: dataset image: assets/background.jpg year: 2015 -published: 2019-2-23 -updated: 2019-2-23 +published: 2019-4-18 +updated: 2019-4-18 authors: Adam Harvey ------------ @@ -19,22 +19,81 @@ authors: Adam Harvey ### sidebar ### end sidebar +Microsoft Celeb (MS Celeb) is a dataset of 10 million face images scraped from the Internet and used for research and development of large-scale biometric recognition systems. According to Microsoft Research who created and published the [dataset](http://msceleb.org) in 2016, MS Celeb is the largest publicly available face recognition dataset in the world, containing over 10 million images of nearly 100,000 individuals. Microsoft's goal in building this dataset was to distribute the initial training dataset of 100,000 individuals images and use this to accelerate reserch into recognizing a target list of one million individuals from their face images "using all the possibly collected face images of this individual on the web as training data".[^msceleb_orig] -https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology +These one million people, defined as Micrsoft Research as "celebrities", are often merely people who must maintain an online presence for their professional lives. Microsoft's list of 1 million people is an expansive exploitation of the current reality that for many people including academics, policy makers, writers, artists, and especially journalists maintaining an online presence is mandatory and should not allow Microsoft (or anyone else) to use their biometrics for reserach and development of surveillance technology. Many of names in target list even include people critical of the very technology Microsoft is using their name and biometric information to build. The list includes digital rights activists like Jillian York and [add more]; artists critical of surveillance including Trevor Paglen, Hito Steryl, Kyle McDonald, Jill Magid, and Aram Bartholl; Intercept founders Laura Poitras, Jeremy Scahill, and Glen Greenwald; Data and Society founder danah boyd; and even Julie Brill the former FTC commissioner responsible for protecting consumer’s privacy to name a few. -https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm +### Microsoft's 1 Million Target List -{% include 'dashboard.html' %} +Below is a list of names that were included in list of 1 million individuals curated to illustrate Microsoft's expansive and exploitative practice of scraping the Internet for biometric training data. The entire name file can be downloaded from [msceleb.org](https://msceleb.org). Names appearing with * indicate that Microsoft also distributed imaged. -{% include 'supplementary_header.html' %} +[ cleaning this up ] + +=== columns 2 + +| Name | ID | Profession | Images | +| --- | --- | --- | --- | +| Jeremy Scahill | /m/02p_8_n | Journalist | x | +| Jillian York | /m/0g9_3c3 | Digital rights activist | x | +| Astra Taylor | /m/05f6_39 | Author, activist | x | +| Jonathan Zittrain | /m/01f75c | EFF board member | no | +| Julie Brill | x | x | x | +| Jonathan Zittrain | x | x | x | +| Bruce Schneier | m.095js | Cryptologist and author | yes | +| Julie Brill | m.0bs3s9g | x | x | +| Kim Zetter | /m/09r4j3 | x | x | +| Ethan Zuckerman | x | x | x | +| Jill Magid | x | x | x | +| Kyle McDonald | x | x | x | +| Trevor Paglen | x | x | x | +| R. Luke DuBois | x | x | x | + +==== + +| Name | ID | Profession | Images | +| --- | --- | --- | -- | +| Trevor Paglen | x | x | x | +| Ai Weiwei | /m/0278dyq | x | x | +| Jer Thorp | /m/01h8lg | x | x | +| Edward Felten | /m/028_7k | x | x | +| Evgeny Morozov | /m/05sxhgd | Scholar and technology critic | yes | +| danah boyd | /m/06zmx5 | Data and Society founder | x | +| Bruce Schneier | x | x | x | +| Laura Poitras | x | x | x | +| Trevor Paglen | x | x | x | +| Astra Taylor | x | x | x | +| Shoshanaa Zuboff | x | x | x | +| Eyal Weizman | m.0g54526 | x | x | +| Aram Bartholl | m.06_wjyc | x | x | +| James Risen | m.09pk6b | x | x | + +=== end columns + +After publishing this list, researchers from Microsoft Asia then worked with researchers affilliated with China's National University of Defense Technology (controlled by China's Central Military Commission) and used the the MS Celeb dataset for their [research paper](https://www.semanticscholar.org/paper/Faces-as-Lighting-Probes-via-Unsupervised-Deep-Yi-Zhu/b301fd2fc33f24d6f75224e7c0991f4f04b64a65) on using "Faces as Lighting Probes via Unsupervised Deep Highlight Extraction" with potential applications in 3D face recognition. + +In an article published by the Financial Times based on data discovered during this investigation, Samm Sacks (senior fellow at New American and China tech policy expert) commented that this research raised "red flags because of the nature of the technology, the authors affilliations, combined with the what we know about how this technology is being deployed in China right now".[^madhu_ft] -### Additional Information +Four more papers published by SenseTime which also use the MS Celeb dataset raise similar flags. SenseTime is Beijing based company providing surveillance to Chinese authorities including [ add context here ] has been [flagged](https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html) as complicity in potential human rights violations. -- The dataset author spoke about his research at the CVPR conference in 2016 <https://www.youtube.com/watch?v=Nl2fBKxwusQ> +One of the 4 SenseTime papers, "Exploring Disentangled Feature Representation Beyond Face Identification", shows how SenseTime is developing automated face analysis technology to infer race, narrow eyes, nose size, and chin size, all of which could be used to target vulnerable ethnic groups based on their facial appearances.[^disentangled] +Earlier in 2019, Microsoft CEO [Brad Smith](https://blogs.microsoft.com/on-the-issues/2018/12/06/facial-recognition-its-time-for-action/) called for the governmental regulation of face recognition, citing the potential for misuse, a rare admission that Microsoft's surveillance-driven business model had lost its bearing. More recently Smith also [announced](https://www.reuters.com/article/us-microsoft-ai/microsoft-turned-down-facial-recognition-sales-on-human-rights-concerns-idUSKCN1RS2FV) that Microsoft would seemingly take stand against potential misuse and decided to not sell face recognition to an unnamed United States law enforcement agency, citing that their technology was not accurate enough to be used on minorities because it was trained mostly on white male faces. + +What the decision to block the sale announces is not so much that Microsoft has upgraded their ethics, but that it publicly acknolwedged it can't sell a data-driven product without data. Microsoft can't sell face recognition for faces they can't train on. + +Until now, that data has been freely harvested from the Internet and packaged in training sets like MS Celeb, which are overwhelmingly [white](https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html) and [male](https://gendershades.org). Without balanced data, facial recognition contains blind spots. And without datasets like MS Celeb, the powerful yet innaccurate facial recognition services like Microsoft's Azure Cognitive Service also would not be able to see at all. + +Microsoft didn't only create MS Celeb for other researchers to use, they also used it internally. In a publicly available 2017 Microsoft Research project called "([One-shot Face Recognition by Promoting Underrepresented Classes](https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/))", Microsoft leveraged the MS Celeb dataset to analyse their algorithms and advertise the results. Interestingly, the Microsoft's [corporate version](https://www.microsoft.com/en-us/research/publication/one-shot-face-recognition-promoting-underrepresented-classes/) does not mention they used the MS Celeb datset, but the [open-acess version](https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70) of the paper published on arxiv.org that same year explicity mentions that Microsoft Research tested their algorithms "on the MS-Celeb-1M low-shot learning benchmark task." + +We suggest that if Microsoft Research wants biometric data for surveillance research and development, they should start with own researcher's biometric data instead of scraping the Internet for journalists, artists, writers, and academics. + +{% include 'dashboard.html' %} + +{% include 'supplementary_header.html' %} ### Footnotes -[^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. -[^localized_region_context]: Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598. -[^replacement_algorithm]: Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
\ No newline at end of file +[^brad_smith]: Brad Smith cite +[^msceleb_orig]: MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition +[^madhu_ft]: Microsoft worked with Chinese military university on artificial intelligence +[^disentangled]: "Exploring Disentangled Feature Representation Beyond Face Identification"
\ No newline at end of file diff --git a/site/content/pages/datasets/oxford_town_centre/index.md b/site/content/pages/datasets/oxford_town_centre/index.md index c32cd022..21d3d949 100644 --- a/site/content/pages/datasets/oxford_town_centre/index.md +++ b/site/content/pages/datasets/oxford_town_centre/index.md @@ -19,7 +19,7 @@ authors: Adam Harvey ### sidebar ### end sidebar -The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.[^ben_benfold_orig] The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009[^guiding_surveillance] the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more. +The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.[^ben_benfold_orig] The CCTV video was obtained from a surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009[^guiding_surveillance] the [Oxford Town Centre dataset](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more. The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating they neither knew of or consented to participation in the research project. @@ -29,9 +29,9 @@ The Oxford Town Centre dataset is unique in that it uses footage from a public s ### Location -The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs [source](https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656). At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their [research projects](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) it is likely that they would also be able to access to this camera. +The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs [source](https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656). At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their [research projects](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html) it is increasingly likely that they would also be able to access to this camera. -To discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera [pointing in the same direction](https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/) as the Oxford Town Centre dataset proving the camera can and has been rotated before. +Next, to discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera [pointing in the same direction](https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/) as the Oxford Town Centre dataset proving the camera can and has been rotated before. As for the capture date, the text on the storefront display shows a sale happening from December 2nd – 7th indicating the capture date was between or just before those dates. The capture year is either 2008 or 2007 since prior to 2007 the Carphone Warehouse ([photo](https://www.flickr.com/photos/katieportwin/364492063/in/photolist-4meWFE-yd7rw-yd7X6-5sDHuc-yd7DN-59CpEK-5GoHAc-yd7Zh-3G2uJP-yd7US-5GomQH-4peYpq-4bAEwm-PALEr-58RkAp-5pHEkf-5v7fGq-4q1J9W-4kypQ2-5KX2Eu-yd7MV-yd7p6-4McgWb-5pJ55w-24N9gj-37u9LK-4FVcKQ-a81Enz-5qNhTG-59CrMZ-2yuwYM-5oagH5-59CdsP-4FVcKN-4PdxhC-5Lhr2j-2PAd2d-5hAwvk-zsQSG-4Cdr4F-3dUPEi-9B1RZ6-2hv5NY-4G5qwP-HCHBW-4JiuC4-4Pdr9Y-584aEV-2GYBEc-HCPkp/), [history](http://www.oxfordhistory.org.uk/cornmarket/west/47_51.html)) did not exist at this location. Since the sweaters in the GAP window display are more similar to those in a [GAP website snapshot](web.archive.org/web/20081201002524/http://www.gap.com/) from November 2007, our guess is that the footage was obtained during late November or early December 2007. The lack of street vendors and slight waste residue near the bench suggests that is was probably a weekday after rubbish removal. diff --git a/site/content/pages/datasets/uccs/index.md b/site/content/pages/datasets/uccs/index.md index de2cec4d..d37db132 100644 --- a/site/content/pages/datasets/uccs/index.md +++ b/site/content/pages/datasets/uccs/index.md @@ -9,8 +9,8 @@ image: assets/background.jpg cssclass: dataset image: assets/background.jpg slug: uccs -published: 2019-2-23 -updated: 2019-4-15 +published: 2019-4-18 +updated: 2019-4-19 authors: Adam Harvey ------------ diff --git a/site/content/pages/research/02_what_computers_can_see/index.md b/site/content/pages/research/02_what_computers_can_see/index.md index 51621f46..faa4ab17 100644 --- a/site/content/pages/research/02_what_computers_can_see/index.md +++ b/site/content/pages/research/02_what_computers_can_see/index.md @@ -25,6 +25,13 @@ A list of 100 things computer vision can see, eg: - tired, drowsiness in car - affectiva: interest in product, intent to buy +## From SenseTime paper + +Exploring Disentangled Feature Representation Beyond Face Identification + +From https://arxiv.org/pdf/1804.03487.pdf +The attribute IDs from 1 to 40 corre-spond to: ‘5 o Clock Shadow’, ‘Arched Eyebrows’, ‘Attrac-tive’, ‘Bags Under Eyes’, ‘Bald’, ‘Bangs’, ‘Big Lips’, ‘BigNose’, ‘Black Hair’, ‘Blond Hair’, ‘Blurry’, ‘Brown Hair’,‘Bushy Eyebrows’, ‘Chubby’, ‘Double Chin’, ‘Eyeglasses’,‘Goatee’, ‘Gray Hair’, ‘Heavy Makeup’, ‘High Cheek-bones’, ‘Male’, ‘Mouth Slightly Open’, ‘Mustache’, ‘Nar-row Eyes’, ‘No Beard’, ‘Oval Face’, ‘Pale Skin’, ‘PointyNose’, ‘Receding Hairline’, ‘Rosy Cheeks’, ‘Sideburns’,‘Smiling’, ‘Straight Hair’, ‘Wavy Hair’, ‘Wearing Ear-rings’, ‘Wearing Hat’, ‘Wearing Lipstick’, ‘Wearing Neck-lace’, ‘Wearing Necktie’ and ‘Young’. It’ + ## From PubFig Dataset |