ACLU and over 70 groups urge DHS to halt use of Clearview AI
The American Civil Liberties Union (ACLU) has joined over 70 other rights and advocacy groups in calling for the Department of Homeland Security (DHS) to halt the use of Clearview AI’s controversial facial recognition system.
In a letter addressed to DHS Secretary Alejandro Mayorkas, the director of the White House's Domestic Policy Council, the ACLU, Electronic Frontier Foundation, OpenMedia and other organisations argue "the use of Clearview AI by federal immigration authorities has not been subject to sufficient oversight or transparency.”
The signatories wrote: “The undersigned organizations have serious concerns about the federal government’s use of facial recognition technology provided by private company Clearview AI. We request that the Department immediately stop using Clearview AI at its agencies on a contractual, trial, or any other basis.”
Clearview AI’s system has raised alarm among privacy advocates for its use of more than three billion biometric identifiers scraped without permission from websites including Facebook, Instagram, and LinkedIn. Despite the concerns, thousands of state and federal law enforcement agencies have used Clearview AI’s system.
According to reporting and data reviewed by , more than 7,000 individuals from nearly 2,000 public agencies nationwide have used Clearview AI to search through millions of Americans’ faces, looking for people, including Black Lives Matter protesters, Capitol insurrectionists, and their own friends and family members.
“Clearview AI’s continued violation of civil rights and privacy rights provide ample reason to discontinue its use.” added the signatories.
UK and Australia
Last year the UK and Australia launched a joint probe into Clearview AI’s mass data scraping.
“The Office of the Australian Information Commissioner (OAIC) and the UK’s Information Commissioner’s Office (ICO) have opened a joint investigation into the personal information handling practices of Clearview Inc., focusing on the company’s use of ‘scraped’ data and biometrics of individuals,” the ICO wrote in a .
“The investigation highlights the importance of enforcement cooperation in protecting the personal information of Australian and UK citizens in a globalized data environment.”
AI has the potential to transform industries and propel advacements in certain areas, but 'high risk' AI has come under scrutiny from many governements and organisations around with world, with worries about privacy and data. Regualtions, rules and laws are often the solutions put in place to protect individuals from these potential risks.
Facebook Develops AI to Crackdown on Deepfakes
In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale?
Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.”
The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with.
Why Reverse Engineering?
Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient.
First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source.
How It Works
Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.
To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters.
What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’.
What Did They Find?
‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising.
Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.