MegaPixels

diff --git a/site/public/datasets/msceleb/assets/notes/index.html b/site/public/datasets/msceleb/assets/notes/index.html new file mode 100644 index 00000000..a249f08b --- /dev/null +++ b/site/public/datasets/msceleb/assets/notes/index.html @@ -0,0 +1,75 @@ + + + + MegaPixels + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Microsoft Celeb 1M is a target list and dataset of web images used for research and development of face recognition

The MS Celeb dataset includes over 10 million images of about 100K people and a target list of 1 million individuals +

Microsoft Celeb 1M is a dataset of 10 millions faces images harvested from the Internet

The MS Celeb dataset includes 100K people and a target list of 1 million individuals

Microsoft Celeb Dataset (MS Celeb)

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 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 an initial training dataset of 100,000 individuals' images to accelerate research into recognizing a larger target list of one million people "using all the possibly collected face images of this individual on the web as training data". 1

These one million people, defined by Microsoft 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. This fact should not allow Microsoft nor anyone else to use their biometrics for research and development of surveillance technology. Many names in the 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; artists critical of surveillance including Trevor Paglen, Jill Magid, and Aram Bartholl; Intercept founders Laura Poitras, Jeremy Scahill, and Glenn Greenwald; Data and Society founder danah boyd; and even Julie Brill, the former FTC commissioner responsible for protecting consumer privacy, to name a few.

These one million people, defined by Microsoft 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, activists, and journalists; maintaining an online presence is mandatory. This fact should not allow Microsoft nor anyone else to use their biometrics for research and development of surveillance technology. Many names in the 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; artists critical of surveillance including Trevor Paglen, Jill Magid, and Aram Bartholl; Intercept founders Laura Poitras, Jeremy Scahill, and Glenn Greenwald; Data and Society founder danah boyd; and even Julie Brill, the former FTC commissioner responsible for protecting consumer privacy, to name only 8 out of 1 million.

Microsoft's 1 Million Target List

Below is a selection of 24 names from the full target list, 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. You can email msceleb@microsoft.com to have your name removed. Names appearing with * indicate that Microsoft also distributed your images.

Below is a selection of 24 names from the full target list, 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. You can email msceleb@microsoft.com to have your name removed. Subjects whose images were distributed by Microsoft are indicated with the total image count. No number indicates the name is only exists in target list.

- + @@ -92,24 +92,24 @@ - - + + - + - - + + - - + + @@ -120,11 +120,11 @@ - + - + @@ -139,13 +139,13 @@

Name	Name (images)	Profession
Journalist
Ai Weiwei*	Artist	Ai Weiwei (220)	Artist, activist
Aram Bartholl	Internet artist	Conceptual artist
Astra Taylor	Author, director, activist
Alexander Madrigal	Journalist	Bruce Schneier (107)	Cryptologist
Bruce Schneier*	Cryptologist	Cory Doctorow (104)	Blogger, journalist
danah boyd	Former FTC Chief Technologist
Evgeny Morozov*	Evgeny Morozov (108)	Tech writer, researcher
Glenn Greenwald*	Glenn Greenwald (86)	Journalist, author

- + - + @@ -169,7 +169,7 @@ - + @@ -181,7 +181,7 @@ - + @@ -194,14 +194,14 @@

Name	Name (images)	Profession
Jeremy Scahill*	Jeremy Scahill (200)	Journalist
Journalist, author
Laura Poitras*	Laura Poitras (104)	Filmmaker
Political blogger
Manal al-Sharif*	Manal al-Sharif (101)	Womens's rights activist

After publishing this list, researchers affiliated with Microsoft Asia then worked with researchers affiliated 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 on using "Faces as Lighting Probes via Unsupervised Deep Highlight Extraction" with potential applications in 3D face recognition.

After publishing this list, researchers affiliated with Microsoft Asia then worked with researchers affiliated with China's National University of Defense Technology (controlled by China's Central Military Commission) and used the MS Celeb image dataset for their research paper on using "Faces as Lighting Probes via Unsupervised Deep Highlight Extraction" with potential applications in 3D face recognition.

In an April 10, 2019 article published by Financial Times based on data surfaced during this investigation, Samm Sacks (a senior fellow at the New America think tank) commented that this research raised "red flags because of the nature of the technology, the author's affiliations, combined with what we know about how this technology is being deployed in China right now". Adding, that "the [Chinese] government is using these technologies to build surveillance systems and to detain minorities [in Xinjiang]". 2

Four more papers published by SenseTime, which also use the MS Celeb dataset, raise similar flags. SenseTime is a computer vision surveillance company that until April 2019 provided surveillance to Chinese authorities to monitor and track Uighur Muslims in Xinjiang province, and had been flagged numerous times as having potential links to human rights violations.

Four more papers published by SenseTime that also use the MS Celeb dataset raise similar flags. SenseTime is a computer vision surveillance company that until April 2019 provided surveillance to Chinese authorities to monitor and track Uighur Muslims in Xinjiang province, and had been flagged numerous times as having potential links to human rights violations.

One of the 4 SenseTime papers, "Exploring Disentangled Feature Representation Beyond Face Identification", shows how SenseTime was 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.

Earlier in 2019, Microsoft President and Chief Legal Officer Brad Smith 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 that Microsoft would seemingly take a stand against such potential misuse, and had decided to not sell face recognition to an unnamed United States agency, citing a lack of accuracy. The software was not suitable to be used on minorities, because it was trained mostly on white male faces.

Earlier in 2019, Microsoft President and Chief Legal Officer Brad Smith 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 that Microsoft would seemingly take a stand against such potential misuse, and had decided to not sell face recognition to an unnamed United States agency, citing a lack of accuracy. In effect, Microsoft's face recognition software was not suitable 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 had upgraded their ethics, but that Microsoft publicly acknowledged it can't sell a data-driven product without data. In other words, 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 and male. Without balanced data, facial recognition contains blind spots. And without datasets like MS Celeb, the powerful yet inaccurate facial recognition services like Microsoft's Azure Cognitive Service also would not be able to see at all.

A visualization of 2,000 of the 100,000 identity included in the image dataset distributed by Microsoft Research. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)

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," Microsoft leveraged the MS Celeb dataset to analyze their algorithms and advertise the results. Interestingly, Microsoft's corporate version of the paper does not mention they used the MS Celeb datset, but the open-access version published on arxiv.org explicitly mentions that Microsoft Research introspected their algorithms "on the MS-Celeb-1M low-shot learning benchmark task."

A visualization of 2,000 of the 100,000 identity included in the image dataset distributed by Microsoft Research. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)

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," Microsoft leveraged the MS Celeb dataset to build their algorithms and advertise the results. Interestingly, Microsoft's corporate version of the paper does not mention they used the MS Celeb datset, but the open-access version published on arxiv.org explicitly mentions that Microsoft Research introspected their algorithms "on the MS-Celeb-1M low-shot learning benchmark task."

We suggest that if Microsoft Research wants to make biometric data publicly available for surveillance research and development, they should start with releasing their researchers' own biometric data, instead of scraping the Internet for journalists, artists, writers, actors, athletes, musicians, and academics.

Who used Microsoft Celeb?

diff --git a/site/public/datasets/uccs/assets/notes/index.html b/site/public/datasets/uccs/assets/notes/index.html new file mode 100644 index 00000000..0218d1b2 --- /dev/null +++ b/site/public/datasets/uccs/assets/notes/index.html @@ -0,0 +1,75 @@ + + + + MegaPixels + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ + + +

+ + + + + \ No newline at end of file diff --git a/site/public/index.html b/site/public/index.html index 81e48a1b..9cb30060 100644 --- a/site/public/index.html +++ b/site/public/index.html @@ -1,16 +1,16 @@ - MegaPixels + MegaPixels: Face Recognition Datasets - + - + diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 64635c55..cb6ff7a7 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -76,6 +76,15 @@

There is only biased feature vector clustering and probabilistic thresholding.

If you don't have data, you don't have a product.

Yesterday's decision by Brad Smith, CEO of Microsoft, to not sell facial recognition to a US law enforcement agency is not an about face by Microsoft to become more humane, it's simply a perfect illustration of the value of training data. Without data, you don't have a product to sell. Microsoft realized that doesn't have enough training data to sell

Cost of Faces

Univ Houston paid subjects $20/ea +http://web.archive.org/web/20170925053724/http://cbl.uh.edu/index.php/pages/research/collecting_facial_images_from_multiples_in_texas

FaceMeta facedataset.com

BASIC: 15,000 images for $6,000 USD
RECOMMENDED: 50,000 images for $12,000 USD
ADVANCED: 100,000 images for $18,000 USD*

Use Your Own Biometrics First

If researchers want faces, they should take selfies and create their own dataset. If researchers want images of families to build surveillance software, they should use and distibute their own family portraits.

Motivation

diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index 7b86f5ef..cc9d3f94 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -92,6 +92,10 @@

100x100 all you need for medical diagnosis

100x100 0.5% of one Instagram photo

Notes:

Google FaceNet used images with (face?) sizes: Input sizes range from 96x96 pixels to 224x224pixels in our experiments. FaceNet: A Unified Embedding for Face Recognition and Clustering https://arxiv.org/pdf/1503.03832.pdf

Ideas:

Find specific cases of facial resolution being used in legal cases, forensic investigations, or military footage

MegaPixels

MegaPixels: Face Recognition Datasets

Date: Wed, 15 May 2019 00:16:48 +0200 Subject: only list image count if present --- site/public/datasets/index.html | 40 ++++++++++++++++++++++++---------------- site/templates/datasets.html | 4 +++- 2 files changed, 27 insertions(+), 17 deletions(-) (limited to 'site/public') diff --git a/site/public/datasets/index.html b/site/public/datasets/index.html index b5fe52ed..8cc5b612 100644 --- a/site/public/datasets/index.html +++ b/site/public/datasets/index.html @@ -66,62 +66,70 @@

- + +

- + +

Person re-identification, multi-camera tracking

- + +

Microsoft Celeb Dataset

2016

Large-scale face recognition

- + +

Oxford Town Centre Dataset

2009

Person detection, gaze estimation

images

2,200

- + +

UnConstrained College Students Dataset

2016

Face recognition, face detection

diff --git a/site/templates/datasets.html b/site/templates/datasets.html index 0c3cbad1..0b1f296e 100644 --- a/site/templates/datasets.html +++ b/site/templates/datasets.html @@ -19,7 +19,9 @@

{{ dataset.meta.dataset.images }} images

+ {% if dataset.meta.dataset.images %} +

{{ dataset.meta.dataset.images }} images

+ {% endif %}

-- cgit v1.2.3-70-g09d2 From f3d56483e743f83d25b15616205dbdd49aad0382 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Wed, 15 May 2019 11:24:33 +0200 Subject: . --- megapixels/commands/site/watch.py | 2 +- notes.md | 70 -------------------------- security.md | 6 --- site/content/_drafts_/lfw/index.md | 9 ++++ site/content/pages/datasets/brainwash/index.md | 16 +++--- site/content/pages/datasets/duke_mtmc/index.md | 7 +-- site/content/pages/datasets/msceleb/index.md | 2 +- site/public/about/attribution/index.html | 2 +- site/public/about/index.html | 2 +- site/public/about/legal/index.html | 2 +- site/public/about/press/index.html | 2 +- site/public/datasets/brainwash/index.html | 12 ++--- site/public/datasets/duke_mtmc/index.html | 11 +++- site/public/datasets/msceleb/index.html | 2 +- site/public/datasets/uccs/index.html | 3 -- 15 files changed, 44 insertions(+), 104 deletions(-) delete mode 100644 notes.md delete mode 100644 security.md (limited to 'site/public') diff --git a/megapixels/commands/site/watch.py b/megapixels/commands/site/watch.py index 7bd71038..d1c75c29 100644 --- a/megapixels/commands/site/watch.py +++ b/megapixels/commands/site/watch.py @@ -35,7 +35,7 @@ def cli(ctx): observer.schedule(SiteBuilder(), path=cfg.DIR_SITE_CONTENT, recursive=True) observer.start() - build_file(cfg.DIR_SITE_CONTENT + "/datasets/lfw/index.md") + #build_file(cfg.DIR_SITE_CONTENT + "/datasets/brainwash/index.md") try: while True: diff --git a/notes.md b/notes.md deleted file mode 100644 index 9dcf3da6..00000000 --- a/notes.md +++ /dev/null @@ -1,70 +0,0 @@ -PATH=/home/adam/torch/install/bin:/home/adam/anaconda3/bin:/home/adam/anaconda3/envs/megapixels/bin:/home/adam/anaconda3/bin:/home/adam/.nvm/versions/node/v9.9.0/bin:/home/adam/bin:/home/adam/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/usr/lib/nvidia/:/usr/local/cuda/bin:/usr/lib/nvidia/:/usr/local/cuda/bin - -PATH=/home/adam/anaconda3/bin:/home/adam/.nvm/versions/node/v9.9.0/bin:/home/adam/code/google-cloud-sdk/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/local/cuda/bin - -CUDA_HOME=/usr/local/cuda -LD_LIBRARY_PATH=/home/adam/torch/install/lib::/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64 - - - -LD_LIBRARY_PATH=/home/adam/torch/install/lib::/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64 - - -LD_LIBRARY_PATH=:/usr/local/cuda/lib64 -CUDA_HOME=/usr/local/cuda - - -TORCH_NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ -TORCH_NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ - - -export PATH=/usr/local/cuda/bin:"$PATH -./clean.sh -export TORCH_NVCC_FLAGS="-D__CUDA_NO_HALF_OPERATORS__" -./install.sh - -find . -name "*.JPEG" | xargs -I {} convert {} -resize "256^>" {} - -find . -name \*.png -exec identify -ping {} \; -or -exec echo {} \; -find . -name \*.jpg -exec identify -ping {} \; -or -exec rm -f {} \; - -luarocks install cudnn -luarocks install display - -scp undisclosed:/home/adam/FIDs.zip . -unzip -q FIDs.zip -find FIDs_NEW -name \*.jpg > list.txt -mkdir -p /work/megapixels_dev/3rdparty/art-DCGAN/fiw/images/ - -while read -r line;do dst=/work/megapixels_dev/3rdparty/art-DCGAN/hipsterwars/images/$(basename "$line");src=`pwd`/$line;ln -s $src $dst;done < list.txt - -extension="${filename##*.}" - - -filename="${filename%.*} - -for d in $(find source -type d) - do - ls $d/*.bin 1>/dev/null 2>&1 && ln -s $d/*.bin target/$(basename $d).dat;done - -gpu=0 batchSize=1 imsize=10 noisemode=linefull net=bedrooms_4_net_G.t7 th generate.lua - -DATA_ROOT=fiw dataset=folder ndf=50 ngf=150 name=fiw_01 nThreads=6 gpu=2 th main.lua - -DATA_ROOT=megaface_13 dataset=folder ndf=50 ngf=150 name=megaface_13 nThreads=6 gpu=1 th main.lua - -DATA_ROOT=hipsterwars dataset=folder ndf=50 ngf=150 name=hipsterwars nThreads=6 gpu=2 th main.lua - -export PATH=/usr/local/cuda/bin/:$PATH - -export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH - - -git clone https://github.com/soumith/cudnn.torch.git -b R7 && cd cudnn.torch && luarocks make cudnn-scm-1.rockspec - -scp undisclosed:/home/adam/hipsterwars_v1.0.zip . -find . -name "*.jpg" -print0 | xargs -0 mogrify -flop - -https://github.com/facebookresearch/deepcluster - -DATA_ROOT=hipsterwars dataset=folder ndf=100 ngf=200 batchSize=128 name=hipsterwars_d100_g200_b128 nThreads=8 gpu=2 th main.lua && DATA_ROOT=hipsterwars dataset=folder ndf=100 ngf=200 batchSize=64 name=hipsterwars_d100_g200_b64 nThreads=8 gpu=2 th main.lua && DATA_ROOT=hipsterwars dataset=folder ndf=100 ngf=300 batchSize=128 name=hipsterwars_d100_g300_b128 nThreads=8 gpu=2 th main.lua && DATA_ROOT=hipsterwars dataset=folder ndf=100 ngf=300 batchSize=64 name=hipsterwars_d100_g200_b64 nThreads=8 gpu=2 th main.lua \ No newline at end of file diff --git a/security.md b/security.md deleted file mode 100644 index d0bffdb4..00000000 --- a/security.md +++ /dev/null @@ -1,6 +0,0 @@ -# MegaPixels - -### Potential Blacklist - -- 103.213.248.154 - - 5,000 hits April with unknown browser from Hong Kong around April 22 \ No newline at end of file diff --git a/site/content/_drafts_/lfw/index.md b/site/content/_drafts_/lfw/index.md index 5d90e87f..ad43e2dd 100644 --- a/site/content/_drafts_/lfw/index.md +++ b/site/content/_drafts_/lfw/index.md @@ -18,6 +18,15 @@ authors: Adam Harvey ### sidebar ### end sidebar + + +## Research notes + +- Used in https://verify.megapixels.cc/paper/feret/verify/8aff9c8a0e17be91f55328e5be5e94aea5227a35https://verify.megapixels.cc/paper/feret/verify/8aff9c8a0e17be91f55328e5be5e94aea5227a35 by Raythen BBN https://en.wikipedia.org/wiki/BBN_Technologies a military contractor +----- + +## Old content + [ PAGE UNDER DEVELOPMENT ] *Labeled Faces in The Wild* (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition[^lfw_www]. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com[^lfw_pingan], LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index 2c51a7b2..e6217a18 100644 --- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -2,8 +2,8 @@ status: published title: Brainwash Dataset -desc: Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014 -subdesc: The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms +desc: Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco +subdesc: The Brainwash dataset includes 11,917 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms slug: brainwash cssclass: dataset image: assets/background.jpg @@ -19,23 +19,25 @@ authors: Adam Harvey ### sidebar ### end sidebar -Brainwash is a dataset of livecam images taken from San Francisco's Brainwash Cafe. It includes 11,918 images of "everyday life of a busy downtown cafe"[^readme] captured at 100 second intervals throughout the entire day. The Brainwash dataset includes 3 full days of webcam images taken on October 27, November 13, and November 24 in 2014. According the author's [research paper](https://www.semanticscholar.org/paper/End-to-End-People-Detection-in-Crowded-Scenes-Stewart-Andriluka/1bd1645a629f1b612960ab9bba276afd4cf7c666) introducing the dataset, the images were acquired with the help of Angelcam.com[^end_to_end] +Brainwash is a dataset of livecam images taken from San Francisco's Brainwash Cafe. It includes 11,917 images of "everyday life of a busy downtown cafe"[^readme] captured at 100 second intervals throughout the entire day. The Brainwash dataset includes 3 full days of webcam images taken on October 27, November 13, and November 24 in 2014. According the author's [research paper](https://www.semanticscholar.org/paper/End-to-End-People-Detection-in-Crowded-Scenes-Stewart-Andriluka/1bd1645a629f1b612960ab9bba276afd4cf7c666) introducing the dataset, the images were acquired with the help of Angelcam.com. [^end_to_end] The Brainwash dataset is unique because it uses images from a publicly available webcam that records people inside a privately owned business without any consent. No ordinary cafe customer would ever suspect that their image would end up in dataset used for surveillance research and development, but that is exactly what happened to customers at Brainwash cafe in San Francisco. -Although Brainwash appears to be a less popular dataset, it was notably used in 2016 and 2017 by researchers affiliated with the National University of Defense Technology in China 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. [^localized_region_context] [^replacement_algorithm]. The [National University of Defense Technology](https://en.wikipedia.org/wiki/National_University_of_Defense_Technology) is controlled by China's top military body, the Central Military Commission. +Although Brainwash appears to be a less popular dataset, it was notably used in 2016 and 2017 by researchers affiliated with the National University of Defense Technology in China 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. [^localized_region_context] [^replacement_algorithm] The [National University of Defense Technology](https://en.wikipedia.org/wiki/National_University_of_Defense_Technology) is controlled by China's top military body, the Central Military Commission. 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". -![caption: Nine of 11,917 images from the the Brainwash dataset. Graphics credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)](assets/brainwash_grid.jpg) +![caption: An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The dataset contains a total of 11,917 images and 81,973 annotated heads. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)](assets/brainwash_example.jpg) + +![caption: A visualization of the active regions for 81,973 head annotations in the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)](assets/brainwash_saliency_map.jpg) + {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} -![caption: An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The dataset contains a total of 11,917 images and 81,973 annotated heads. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)](assets/brainwash_example.jpg) -![caption: A visualization of the active regions for 81,973 head annotations in the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)](assets/brainwash_saliency_map.jpg) +![caption: Nine of 11,917 images from the the Brainwash dataset. Graphics credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)](assets/brainwash_grid.jpg) {% include 'cite_our_work.html' %} diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index 11414fd3..928c79fa 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -8,7 +8,7 @@ slug: duke_mtmc cssclass: dataset image: assets/background.jpg published: 2019-4-18 -updated: 2019-4-18 +updated: 2019-05-22 authors: Adam Harvey ------------ @@ -20,13 +20,13 @@ authors: Adam Harvey Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60 FPS, with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy"[^duke_mtmc_orig]. -In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims. +In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims. In one 2018 [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf) jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled [Attention-Aware Compositional Network for Person Re-identification](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e), the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. [^xinjiang_nyt][^sensetime_qz][^sensenets_uyghurs] ![caption: A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.](assets/duke_mtmc_reid_montage.jpg) -Despite [repeated](https://www.hrw.org/news/2017/11/19/china-police-big-data-systems-violate-privacy-target-dissent) [warnings](https://www.hrw.org/news/2018/02/26/china-big-data-fuels-crackdown-minority-region) by Human Rights Watch that the authoritarian surveillance used in China represents a violation of human rights, researchers at Duke University continued to provide open access to their dataset for anyone to use for any project. As the surveillance crisis in China grew, so did the number of citations with links to organizations complicit in the crisis. In 2018 alone there were over 70 research projects happening in China that publicly acknowledged benefiting from the Duke MTMC dataset. Amongst these were projects from SenseNets, SenseTime, CloudWalk, Megvii, Beihang University, and the PLA's National University of Defense Technology. +Despite [repeated](https://www.hrw.org/news/2017/11/19/china-police-big-data-systems-violate-privacy-target-dissent) [warnings](https://www.hrw.org/news/2018/02/26/china-big-data-fuels-crackdown-minority-region) by Human Rights Watch that the authoritarian surveillance used in China represents humanitarian crisis, researchers at Duke University continued to provide open access to their dataset for anyone to use for any project. As the surveillance crisis in China grew, so did the number of citations with links to organizations complicit in the crisis. In 2018 alone there were over 90 research projects happening in China that publicly acknowledged using and benefiting from the Duke MTMC dataset. Amongst these were projects from CloudWalk, Hikvision, Megvii (Face++), SenseNets, SenseTime, Beihang University, and the PLA's National University of Defense Technology. | Organization | Paper | Link | Year | Used Duke MTMC | |---|---|---|---| @@ -34,6 +34,7 @@ Despite [repeated](https://www.hrw.org/news/2017/11/19/china-police-big-data-sys | Beihang University | Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology | [acm.org](https://dl.acm.org/citation.cfm?id=3240663) | 2018 | ✔ | | CloudWalk | CloudWalk re-identification technology extends facial biometric tracking with improved accuracy | [BiometricUpdate.com](https://www.biometricupdate.com/201903/cloudwalk-re-identification-technology-extends-facial-biometric-tracking-with-improved-accuracy) | 2019 | ✔ | |CloudWalk| Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/pdf/1804.05275.pdf) | 2018 | ✔ | +| Hikvision | Learning Incremental Triplet Margin for Person Re-identification | [arxiv.org](https://arxiv.org/abs/1812.06576) | 2018 | ✔ | | Megvii | Person Re-Identification (slides) | [github.io](https://zsc.github.io/megvii-pku-dl-course/slides/Lecture%2011,%20Human%20Understanding_%20ReID%20and%20Pose%20and%20Attributes%20and%20Activity%20.pdf) | 2017 | ✔ | | Megvii | Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project | [SemanticScholar](https://www.semanticscholar.org/paper/Multi-Target%2C-Multi-Camera-Tracking-by-Hierarchical-Zhang-Wu/10c20cf47d61063032dce4af73a4b8e350bf1128) | 2018 | ✔ | | Megvii | SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identification | [arxiv.org](https://arxiv.org/abs/1810.06996) | 2018 | ✔ | diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md index 5f48ebfd..d1542f3f 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -64,7 +64,7 @@ Below is a selection of 24 names from the full target list, curated to illustrat === end columns -After publishing this list, researchers affiliated with Microsoft Asia then worked with researchers affiliated with China's [National University of Defense Technology](https://en.wikipedia.org/wiki/National_University_of_Defense_Technology) (controlled by China's Central Military Commission) and used the MS Celeb image dataset for their research paper on using "[Faces as Lighting Probes via Unsupervised Deep Highlight Extraction]((https://www.semanticscholar.org/paper/Faces-as-Lighting-Probes-via-Unsupervised-Deep-Yi-Zhu/b301fd2fc33f24d6f75224e7c0991f4f04b64a65)" with potential applications in 3D face recognition. +After publishing this list, researchers affiliated with Microsoft Asia then worked with researchers affiliated with China's [National University of Defense Technology](https://en.wikipedia.org/wiki/National_University_of_Defense_Technology) (controlled by China's Central Military Commission) and used the MS Celeb image dataset for their research paper on using "[Faces as Lighting Probes via Unsupervised Deep Highlight Extraction](https://www.semanticscholar.org/paper/Faces-as-Lighting-Probes-via-Unsupervised-Deep-Yi-Zhu/b301fd2fc33f24d6f75224e7c0991f4f04b64a65)" with potential applications in 3D face recognition. In an April 10, 2019 [article](https://www.ft.com/content/9378e7ee-5ae6-11e9-9dde-7aedca0a081a) published by Financial Times based on data surfaced during this investigation, Samm Sacks (a senior fellow at the New America think tank) commented that this research raised "red flags because of the nature of the technology, the author's affiliations, combined with what we know about how this technology is being deployed in China right now". Adding, that "the [Chinese] government is using these technologies to build surveillance systems and to detain minorities [in Xinjiang]".[^madhu_ft] diff --git a/site/public/about/attribution/index.html b/site/public/about/attribution/index.html index aec1b0f8..29c220e4 100644 --- a/site/public/about/attribution/index.html +++ b/site/public/about/attribution/index.html @@ -57,7 +57,7 @@

diff --git a/site/public/about/index.html b/site/public/about/index.html index 9b3e455b..288aa2aa 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -57,7 +57,7 @@

diff --git a/site/public/about/legal/index.html b/site/public/about/legal/index.html index df092153..d8d81d04 100644 --- a/site/public/about/legal/index.html +++ b/site/public/about/legal/index.html @@ -57,7 +57,7 @@

diff --git a/site/public/about/press/index.html b/site/public/about/press/index.html index febc1256..57a07449 100644 --- a/site/public/about/press/index.html +++ b/site/public/about/press/index.html @@ -57,7 +57,7 @@

diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 8ae6b122..4fcea807 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -4,7 +4,7 @@ MegaPixels - + @@ -53,7 +53,7 @@

Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014

The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms +

Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco

The Brainwash dataset includes 11,917 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms

Brainwash Dataset

Brainwash is a dataset of livecam images taken from San Francisco's Brainwash Cafe. It includes 11,918 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throughout the entire day. The Brainwash dataset includes 3 full days of webcam images taken on October 27, November 13, and November 24 in 2014. According the author's research paper introducing the dataset, the images were acquired with the help of Angelcam.com 2

Brainwash is a dataset of livecam images taken from San Francisco's Brainwash Cafe. It includes 11,917 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throughout the entire day. The Brainwash dataset includes 3 full days of webcam images taken on October 27, November 13, and November 24 in 2014. According the author's research paper introducing the dataset, the images were acquired with the help of Angelcam.com. 2

The Brainwash dataset is unique because it uses images from a publicly available webcam that records people inside a privately owned business without any consent. No ordinary cafe customer would ever suspect that their image would end up in dataset used for surveillance research and development, but that is exactly what happened to customers at Brainwash cafe in San Francisco.

Although Brainwash appears to be a less popular dataset, it was notably used in 2016 and 2017 by researchers affiliated with the National University of Defense Technology in China for two research projects on advancing the capabilities of object detection to more accurately isolate the target region in an image. 3 4. The National University of Defense Technology is controlled by China's top military body, the Central Military Commission.

Although Brainwash appears to be a less popular dataset, it was notably used in 2016 and 2017 by researchers affiliated with the National University of Defense Technology in China for two research projects on advancing the capabilities of object detection to more accurately isolate the target region in an image. 3 4 The National University of Defense Technology is controlled by China's top military body, the Central Military Commission.

The dataset also appears in a 2017 research paper from Peking University for the purpose of improving surveillance capabilities for "people detection in the crowded scenes".

Nine of 11,917 images from the the Brainwash dataset. Graphics credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)

An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The dataset contains a total of 11,917 images and 81,973 annotated heads. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)

A visualization of the active regions for 81,973 head annotations in the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)

Who used Brainwash Dataset?

@@ -140,7 +140,7 @@

Supplementary Information

A visualization of the active regions for 81,973 head annotations in the Brainwash dataset training partition. Credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)

Nine of 11,917 images from the the Brainwash dataset. Graphics credit: megapixels.cc. License: Open Data Commons Public Domain Dedication (PDDL)

Cite Our Work

diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 24ee6cc2..16d11cb0 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -74,9 +74,9 @@

Website

duke.edu

Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60 FPS, with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy" 1.

In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims.

In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims.

In one 2018 paper jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled Attention-Aware Compositional Network for Person Re-identification, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. 4 2 3

Name	Hito Steyerl	Artist, writer
James Risen	Journalist

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If you don't have data, you don't have a product.

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Microsoft Celeb Dataset (MS Celeb)

Microsoft Celeb Dataset (MS Celeb)

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