taylor swift

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Taylor Swift fans dancing and jumping created last year’s “Swift quakes”

Good vibrations —

“Shake It Off” produced tremors equivalent to a local magnitude earthquake of 0.851.

Taylor Swift on the Eras Tour in 2023

Enlarge / Taylor Swift during her Eras Tour. Crowd motions likely caused mini “Swift quakes” recorded by seismic monitoring stations.

When mega pop star Taylor Swift gave a series of concerts last August at the SoFi Stadium in Los Angeles, regional seismic network stations recorded unique harmonic vibrations known as “concert tremor.” A similar “Swift quake” had occurred the month before in Seattle, prompting scientists from the California Institute of Technology and UCLA to take a closer look at seismic data collected during Swift’s LA concert.

The researchers concluded that the vibrations were largely generated by crowd motion as “Swifties” jumped and danced enthusiastically to the music and described their findings in a new paper published in the journal Seismological Research Letters. The authors contend that gaining a better understanding of atypical seismic signals like those generated by the Swift concert could improve the analysis of seismic signals in the future, as well as bolster emerging applications like using signals from train noise for seismic interferometry.

Concert tremor consists of low-frequency signals of extended duration with harmonic frequency peaks between 1 and 10 Hz, similar to the signals generated by volcanoes or trains. There has been considerable debate about the source of these low-frequency concert tremor signals: Are they produced by the synchronized movement of the crowd, or by the sound systems or instruments coupled to the stage? Several prior studies of stadium concerts have argued for the former hypothesis, while a 2015 study found that a chanting crowd at a football game produced similar harmonic seismic tremors. However, a 2008 study concluded that such signals generated during an outdoor electronic dance music festival came from the sound system vibrating to the musical beat.

The Caltech/UCLA team didn’t just rely on the data from the regional network stations. The scientists placed additional motion sensors throughout the stadium prior to the concert, enabling them to characterize all the seismic signals produced during the concert. The signals had such unique characteristics that it was relatively easy to identify them with a spectrogram. In fact, the authors were able to identify 43 of the 45 songs Swift performed based on the distinctive signal of each song.

They also calculated how much radiated energy was produced by each song. “Shake It Off” produced the most radiated energy, equivalent to a local magnitude earthquake of 0.851. “Keep in mind this energy was released over a few minutes compared to a second for an earthquake of that size,” said co-author Gabrielle Tepp of Caltech.

Tepp is a volcanologist and musician in her own right. That combination came in handy when it was time to conduct a lab-based experiment to test the team’s source hypothesis using a portable public announcement speaker system. They played Swift’s “Love Story” and Tepp gamely danced and jumped with the beat during the last chorus while sensors recorded the seismic vibrations. “Even though I was not great at staying in the same place—I ended up jumping around in a small circle, like at a concert—I was surprised at how clear the signal came out,” said Tepp. They also tested a steady beat as Tepp played her bass guitar in order to isolate the signal from a single instrument.

The resulting fundamental harmonic during the jumping was consistent with the song’s beat rate. However, the bass beats didn’t produce a harmonic signal, which was surprising since those beats were better synchronized with the actual musical beats than Tepp’s jumping motions. This might be due to the rounder shape of the bass beat signals compared to sharper spiking signals in response to the jumping.

Map showing the concert venue and nearby seismic stations (circles) that recorded signals from the Swift concerts (blue).

Enlarge / Map showing the concert venue and nearby seismic stations (circles) that recorded signals from the Swift concerts (blue).

Gabrielle Tepp et al., 2024

The authors noted that their experiment did not involve a stage or stadium-grade sound system, “so we cannot completely rule out loudspeakers as a vibrational energy source,” they wrote. Nonetheless, “Overall the evidence suggests that crowd movement is the primary source of the low-frequency signals, with the speaker system or instruments potentially contributing via stage of building vibrations.” The fact that the same kind of low-frequency seismic signals were not detected during pre-concert sound checks seems to support that conclusion, although there were higher frequency signals during sound checks.

The team also studied the structural response of the stadium and conducted a similar analysis of seismic readings from three other concerts at SoFi Stadium that summer: country music’s Morgan Waller, Beyoncé, and Metallica, as well as picking up clear signals at one monitoring station for the three opening acts: Pantera, DJ Khaled, and Five Finger Death Punch, respectively. The results were markedly similar to the seismic data gathered from the Taylor Swift concerts, although none of the signals matched the strongest of those detected during the Swift concerts.

The researchers were surprised to find that the seismic signals from the Metallica concert were the weakest among all the concerts and markedly different from the others, “slanted and kind of weird looking,” per Tepp. They found several comments in music forums from fans complaining about poor sound quality at the Metallica concert. “If fans had a hard time discerning the song or beat, it may explain the more variable signals because it would have influenced their movements,” the authors wrote.

It’s also possible that heavy metal live performances are less tightly choreographed than Beyoncé or Swift performances, or that heavy metal fans don’t move with the music in quite the same way. “Metal fans like to headbang a lot, so they’re not necessarily bouncing,” said Tepp. “It might just be that the ways in which they move don’t create as strong of a signal.”

Seismological Research Letters, 2024. DOI: 10.1785/0220230385  (About DOIs).

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4chan daily challenge sparked deluge of explicit AI Taylor Swift images

4chan daily challenge sparked deluge of explicit AI Taylor Swift images

4chan users who have made a game out of exploiting popular AI image generators appear to be at least partly responsible for the flood of fake images sexualizing Taylor Swift that went viral last month.

Graphika researchers—who study how communities are manipulated online—traced the fake Swift images to a 4chan message board that’s “increasingly” dedicated to posting “offensive” AI-generated content, The New York Times reported. Fans of the message board take part in daily challenges, Graphika reported, sharing tips to bypass AI image generator filters and showing no signs of stopping their game any time soon.

“Some 4chan users expressed a stated goal of trying to defeat mainstream AI image generators’ safeguards rather than creating realistic sexual content with alternative open-source image generators,” Graphika reported. “They also shared multiple behavioral techniques to create image prompts, attempt to avoid bans, and successfully create sexually explicit celebrity images.”

Ars reviewed a thread flagged by Graphika where users were specifically challenged to use Microsoft tools like Bing Image Creator and Microsoft Designer, as well as OpenAI’s DALL-E.

“Good luck,” the original poster wrote, while encouraging other users to “be creative.”

OpenAI has denied that any of the Swift images were created using DALL-E, while Microsoft has continued to claim that it’s investigating whether any of its AI tools were used.

Cristina López G., a senior analyst at Graphika, noted that Swift is not the only celebrity targeted in the 4chan thread.

“While viral pornographic pictures of Taylor Swift have brought mainstream attention to the issue of AI-generated non-consensual intimate images, she is far from the only victim,” López G. said. “In the 4chan community where these images originated, she isn’t even the most frequently targeted public figure. This shows that anyone can be targeted in this way, from global celebrities to school children.”

Originally, 404 Media reported that the harmful Swift images appeared to originate from 4chan and Telegram channels before spreading on X (formerly Twitter) and other social media. Attempting to stop the spread, X took the drastic step of blocking all searches for “Taylor Swift” for two days.

But López G. said that Graphika’s findings suggest that platforms will continue to risk being inundated with offensive content so long as 4chan users are determined to continue challenging each other to subvert image generator filters. Rather than expecting platforms to chase down the harmful content, López G. recommended that AI companies should get ahead of the problem, taking responsibility for outputs by paying attention to evolving tactics of toxic online communities reporting precisely how they’re getting around safeguards.

“These images originated from a community of people motivated by the ‘challenge’ of circumventing the safeguards of generative AI products, and new restrictions are seen as just another obstacle to ‘defeat,’” López G. said. “It’s important to understand the gamified nature of this malicious activity in order to prevent further abuse at the source.”

Experts told The Times that 4chan users were likely motivated to participate in these challenges for bragging rights and to “feel connected to a wider community.”

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X can’t stop spread of explicit, fake AI Taylor Swift images

Escalating the situation —

Will Swifties’ war on AI fakes spark a deepfake porn reckoning?

X can’t stop spread of explicit, fake AI Taylor Swift images

Explicit, fake AI-generated images sexualizing Taylor Swift began circulating online this week, quickly sparking mass outrage that may finally force a mainstream reckoning with harms caused by spreading non-consensual deepfake pornography.

A wide variety of deepfakes targeting Swift began spreading on X, the platform formerly known as Twitter, yesterday.

Ars found that some posts have been removed, while others remain online, as of this writing. One X post was viewed more than 45 million times over approximately 17 hours before it was removed, The Verge reported. Seemingly fueling more spread, X promoted these posts under the trending topic “Taylor Swift AI” in some regions, The Verge reported.

The Verge noted that since these images started spreading, “a deluge of new graphic fakes have since appeared.” According to Fast Company, these harmful images were posted on X but soon spread to other platforms, including Reddit, Facebook, and Instagram. Some platforms, like X, ban sharing of AI-generated images but seem to struggle with detecting banned content before it becomes widely viewed.

Ars’ AI reporter Benj Edwards warned in 2022 that AI image-generation technology was rapidly advancing, making it easy to train an AI model on just a handful of photos before it could be used to create fake but convincing images of that person in infinite quantities. That is seemingly what happened to Swift, and it’s currently unknown how many different non-consensual deepfakes have been generated or how widely those images have spread.

It’s also unknown what consequences have resulted from spreading the images. At least one verified X user had their account suspended after sharing fake images of Swift, The Verge reported, but Ars reviewed posts on X from Swift fans targeting others who allegedly shared images whose accounts remain active. Swift fans also have been uploading countless favorite photos of Swift to bury the harmful images and prevent them from appearing in various X searches. Her fans seem dedicated to reducing the spread however they can, with some posting different addresses, seemingly in attempts to dox an X user who, they’ve alleged, is the initial source of the images.

Neither X nor Swift’s team has yet commented on the deepfakes, but it seems clear that solving the problem will require more than just requesting removals from social media platforms. The AI model trained on Swift’s images is likely still out there, likely procured through one of the known websites that specialize in making fine-tuned celebrity AI models. As long as the model exists, anyone with access could crank out as many new images as they wanted, making it hard for even someone with Swift’s resources to make the problem go away for good.

In that way, Swift’s predicament might raise awareness of why creating and sharing non-consensual deepfake pornography is harmful, perhaps moving the culture away from persistent notions that nobody is harmed by non-consensual AI-generated fakes.

Swift’s plight could also inspire regulators to act faster to combat non-consensual deepfake porn. Last year, she inspired a Senate hearing after a Live Nation scandal frustrated her fans, triggering lawmakers’ antitrust concerns about the leading ticket seller, The New York Times reported.

Some lawmakers are already working to combat deepfake porn. Congressman Joe Morelle (D-NY) proposed a law criminalizing deepfake porn earlier this year after teen boys at a New Jersey high school used AI image generators to create and share non-consensual fake nude images of female classmates. Under that proposed law, anyone sharing deepfake pornography without an individual’s consent risks fines and being imprisoned for up to two years. Damages could go as high as $150,000 and imprisonment for as long as 10 years if sharing the images facilitates violence or impacts the proceedings of a government agency.

Elsewhere, the UK’s Online Safety Act restricts any illegal content from being shared on platforms, including deepfake pornography. It requires moderation, or companies will risk fines worth more than $20 million, or 10 percent of their global annual turnover, whichever amount is higher.

The UK law, however, is controversial because it requires companies to scan private messages for illegal content. That makes it practically impossible for platforms to provide end-to-end encryption, which the American Civil Liberties Union has described as vital for user privacy and security.

As regulators tangle with legal questions and social media users with moral ones, some AI image generators have moved to limit models from producing NSFW outputs. Some did this by removing some of the large quantity of sexualized images in the models’ training data, such as Stability AI, the company behind Stable Diffusion. Others, like Microsoft’s Bing image creator, make it easy for users to report NSFW outputs.

But so far, keeping up with reports of deepfake porn seems to fall squarely on social media platforms’ shoulders. Swift’s battle this week shows how unprepared even the biggest platforms currently are to handle blitzes of harmful images seemingly uploaded faster than they can be removed.

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