Her face was deepfaked onto porn. When police wouldn't help, she did her own investigation 26%

By Vanessa Caldwell0%

11/22/2023, 7:00:44 AM

BS Summary: This article contains 31 faulty reasoning types, including Hasty Generalization, Anecdotal, and Biased Writer Voice, with Negativity Bias as the most egregious example at 20.5% saturation with 194 hits. Analysis detected 1,724 faulty-reasoning hits from 948 analyzed words, generating a BS Score of 37.9% and a BS Rank of 26% (16,249 of 21,887 articles). This article is better (less manipulative) than 74.20% of the article peer group.

When 22-year-old Taylor got a Facebook message from a friend with a link, she clicked. 
She couldn't believe what she saw: her own face staring back at her in a hard-core porn video. 
Taylor had been deepfaked, her face digitally pasted onto someone else's body. 
Her likeness appeared in several videos that had been posted using a Pornhub profile impersonating her with her real name, college and hometown. 
In the documentary Another Body, Taylor (whose name and face have been changed to protect her identity) takes viewers on her search to uncover who created and posted the videos. 
As she investigates, she makes an astonishing discovery: she's not the only student from her college who's been targeted with deepfake porn. 
And she soon realizes that no one is safe from this growing threat  celebrities, politicians, classmates, colleagues and friends are all at risk. 
"I want to find out who did this because I want to know why," she says in the film. 
"I want to know what was going through their head when they decided to do this." 
Most jurisdictions lack deepfake laws 
After Taylor discovers the videos online, she calls the police, who struggle to understand what had happened. 
Initially, the detective assigned to the case thinks someone has put real pornographic videos of Taylor online. 
"I was like, 'No, someone put my face on porn online,'" Taylor says. 
"[The officer] seemed really confused as to why that's wrong. 
 He asked, 'What have you done to cause someone to do this to you?' 
 
"After a couple weeks, I had called him again, and he said that it's disgusting what happened, but the person had a right to do it. 
Like, they didn't break any laws, so they had a right to do that." 
They didn't break any laws, so they had a right to do that. 
Taylor reaches out to lawyer Adam Dodge, founder of EndTAB (Ending Tech-Enabled Abuse). 
He explains that there are no laws that address deepfakes in Taylor's state, and no federal laws in the U.S. 
"I trained 500 judges last year on deepfakes, and I would say in excess of 90 per cent of them had never heard of a deepfake before," he says. 
Deepfakes typically don't fall within the parameters of non-consensual pornography laws because it's not the victim's body. 
So lawyers and advocates look for other ways to prosecute, such as false personation. 
However, since people can easily remain anonymous online, it's often difficult to catch the people behind deepfakes. 
"Usually the investigations I see where they are able to catch somebody, there's, like, a second where they didn't have their VPN on. 
And that is the tiny hole in that anonymous shield that [the authorities] are able to get through  and prove who that person is." 
Taylor was not the only one 
As Taylor digs deeper, she discovers that Julia (also a pseudonym), another student from her college, has also been targeted. 
They figure the culprit must be someone they both know. 
The two women have dealt with misogyny and harassment in their male-dominated engineering program before, and determined to uncover who's behind the videos, they take the investigation into their own hands. 
They dive headfirst into the underground world of deepfake technology and discover a society of men terrorizing women. 
As Taylor and Julia whittle down their list of suspects to two  people they know and who would understand deepfake technology  they discover that at least four more women in their circle have been targeted by the same person. 
"The fact that the group of women is this big, it scares me because I have a gut feeling that we haven't even found all of them," Taylor says. 
Only one person links all of these women: Mike, a former close friend, whose obsessive demands for their attention turned sinister. 
It turns out Mike's deepfake accounts get millions of views. 
But he's protected by the anonymity of the internet. 
"I think it's disgusting that he's not going to face any repercussions," Julia says. 
"His future female co-workers and female friends have no idea that he's done this. 
And I think that's really dangerous for the people around him." 
Taylor also learns that Mike created 28 deepfake porn videos of YouTuber Gibi, with over 500,000 views, and she shares this with Gibi in a video call. 
"I know so many people personally that this has happened to, but it's not being talked about for fear of safety, for fear of embarrassment, for fear of, you know, being misunderstood," Gibi says. 
"So I think I'm very tired of ignoring it." 
In November 2022, Gibi spoke out about deepfakes in a YouTube video. 
"Today, I need to talk about something uncomfortable," she said. 
"There is a lot of porn of me on the internet. 
 My face has been digitally plastered countless times over other people's naked bodies to create sex content that I never consented to. 
 It has felt hopeless and honestly unsafe to ever speak out. 
However, I am relieved to finally be talking about this, with some support." 
Hundreds of thousands of videos, and countless woman affected 
After her experience, Taylor gave anonymous testimony during a White House meeting about deepfakes and how to change the laws around them. 
In her video testimony, she said, "What I want you to understand about deepfakes is that there are [countless] women being affected by this right now. 
"You may not be able to see them, but the impacts are very real and very serious." 
Watch Another Body on CBC Gem. 
Article reasoning-pattern comparisonThis article: 7.1%Vanessa Caldwell: 2.9%CBC: 2.6%Confirmation Bias7.1%This article: 0.5%Vanessa Caldwell: 0.1%CBC: 0.7%Anchoring Bias0.5%This article: 8.3%Vanessa Caldwell: 4.0%CBC: 3.1%Availability Heuristic8.3%This article: 6.1%Vanessa Caldwell: 1.1%CBC: 1.1%Representativeness Heuristic6.1%This article: 0.0%Vanessa Caldwell: 0.3%CBC: 1.0%Hindsight Bias0.0%This article: 5.7%Vanessa Caldwell: 2.2%CBC: 1.4%Overconfidence Bias5.7%This article: 8.8%Vanessa Caldwell: 3.1%CBC: 4.2%Framing Effect8.8%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.6%Loss Aversion0.0%This article: 0.9%Vanessa Caldwell: 0.3%CBC: 0.6%Status Quo Bias0.9%This article: 0.0%Vanessa Caldwell: 0.4%CBC: 0.2%Sunk Cost Effect0.0%This article: 1.4%Vanessa Caldwell: 3.6%CBC: 4.0%Optimism Bias1.4%This article: 11.2%Vanessa Caldwell: 2.6%CBC: 1.4%Pessimism Bias11.2%This article: 20.5%Vanessa Caldwell: 10.3%CBC: 5.7%Negativity Bias20.5%This article: 0.0%Vanessa Caldwell: 0.3%CBC: 1.2%Self-Serving Bias0.0%This article: 2.2%Vanessa Caldwell: 1.1%CBC: 0.9%Fundamental Attribution Error2.2%This article: 1.8%Vanessa Caldwell: 0.3%CBC: 0.2%Actor-Observer Bias1.8%This article: 3.3%Vanessa Caldwell: 1.1%CBC: 1.2%In-Group Bias3.3%This article: 1.9%Vanessa Caldwell: 0.9%CBC: 0.3%Out-Group Homogeneity Bias1.9%This article: 1.1%Vanessa Caldwell: 0.5%CBC: 3.6%Halo Effect1.1%This article: 0.0%Vanessa Caldwell: 0.2%CBC: 0.1%Horn Effect0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Vanessa Caldwell: 0.3%CBC: 1.2%Recency Bias0.0%This article: 0.0%Vanessa Caldwell: 0.4%CBC: 0.4%Primacy Effect0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.0%Blind-Spot Bias0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.4%Ad Hominem0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.2%Straw Man0.0%This article: 8.9%Vanessa Caldwell: 2.8%CBC: 3.7%Appeal to Authority8.9%This article: 2.5%Vanessa Caldwell: 1.4%CBC: 1.3%False Dilemma2.5%This article: 0.9%Vanessa Caldwell: 0.2%CBC: 0.4%Slippery Slope0.9%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.2%Circular Reasoning0.0%This article: 16.9%Vanessa Caldwell: 7.6%CBC: 4.8%Hasty Generalization16.9%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.2%Red Herring0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.6%Bandwagon0.0%This article: 13.2%Vanessa Caldwell: 3.6%CBC: 4.2%Appeal to Emotion13.2%This article: 0.0%Vanessa Caldwell: 0.5%CBC: 0.6%Begging the Question0.0%This article: 0.9%Vanessa Caldwell: 0.6%CBC: 2.5%Post Hoc (False Cause)0.9%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.0%Tu Quoque0.0%This article: 3.0%Vanessa Caldwell: 0.5%CBC: 0.3%Burden of Proof3.0%This article: 1.8%Vanessa Caldwell: 0.3%CBC: 0.3%Appeal to Nature1.8%This article: 4.4%Vanessa Caldwell: 0.7%CBC: 0.3%Composition/Division4.4%This article: 15.1%Vanessa Caldwell: 3.4%CBC: 3.3%Anecdotal15.1%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.1%No True Scotsman0.0%This article: 8.8%Vanessa Caldwell: 2.6%CBC: 1.6%Ambiguity (Equivocation)8.8%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.1%Middle Ground0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.1%Personal Incredulity0.0%This article: 4.1%Vanessa Caldwell: 0.7%CBC: 0.1%Special Pleading4.1%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.1%Genetic Fallacy0.0%This article: 1.5%Vanessa Caldwell: 0.7%CBC: 1.1%Unattributed Quote1.5%This article: 0.0%Vanessa Caldwell: 0.6%CBC: 0.8%Quote-first Misdirection0.0%This article: 13.5%Vanessa Caldwell: 4.2%CBC: 4.8%Biased Writer Voice13.5%This article: 2.7%Vanessa Caldwell: 0.7%CBC: 2.4%Indoctrination2.7%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.4%Politically Left Leaning Bias0.0%This article: 0.0%Vanessa Caldwell: 0.0%CBC: 0.1%Politically Right Leaning Bias0.0%This article: 3.0%Vanessa Caldwell: 3.2%CBC: 1.7%Attempt to Sell a Product or S…3.0%

948 words analyzed.

Speakers

5speakers46%attributed speech509writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 6 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 24 words • 100.0% coverageTaylor • 19 words • 0.0% coverageTaylor • 16 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageTaylor • 13 words • 0.0% coverageTaylor • 10 words • 0.0% coverageTaylor • 15 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageTaylor • 26 words • 0.0% coverageTaylor • 14 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageAdam Dodge • 20 words • 0.0% coverageAdam Dodge • 29 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageAdam Dodge • 23 words • 0.0% coverageAdam Dodge • 25 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 41 words • 0.0% coverageTaylor • 29 words • 0.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageJulia • 14 words • 0.0% coverageJulia • 14 words • 100.0% coverageJulia • 11 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageGibi • 34 words • 100.0% coverageGibi • 9 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageGibi • 10 words • 0.0% coverageGibi • 11 words • 0.0% coverageGibi • 23 words • 0.0% coverageGibi • 12 words • 0.0% coverageGibi • 13 words • 0.0% coverageWriter's voice • 9 words • 100.0% coverageWriter's voice • 22 words • 100.0% coverageTaylor • 26 words • 100.0% coverageTaylor • 17 words • 0.0% coverageCBC Gem • 6 words • 100.0% coverage
Selected voice

Gibi

100%flagged-word coverage
112 attributed words26% of attributed speech81% writer coverage
0%17.5%35.0%Biased Writer Voice+11.9 ptsWriter: 18.5%Gibi: 30.4%30.4%Attempt to Sell a Product -4.3 ptsWriter: 4.3%Gibi: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.