KUER0%

Think you can spot a deepfake? Utah Valley University says it’s getting harder 39%

By Hugo Rikard-Bell0%

6/18/2026, 11:28:45 PM

BS Summary: This article contains 24 faulty reasoning types, including Hasty Generalization, Ambiguity (Equivocation), and Anecdotal, with Negativity Bias as the most egregious example at 18.2% saturation with 119 hits. Analysis detected 1,114 faulty-reasoning hits from 654 analyzed words, generating a BS Score of 44.3% and a BS Rank of 39% (13,535 of 21,886 articles). This article is better (less manipulative) than 61.80% of the article peer group.

If you were scrolling through social media a few years ago, you may have come across a video of a politician or celebrity that looked like a detailed video game animation. 
It might have appeared real at first glance, but on closer inspection, the mouth moved unnaturally, or the person had six fingers. 
These were common signs the content was a deepfake  an AI-generated image, video or audio manipulated to replicate a real person or event, often to spread misinformation. 
Those tells are now a lot harder to detect, according to a new study from Utah Valley University's Center for National Security Studies. 
Researchers gave an online survey with a mix of deepfakes and real content to over 600 test subjects. 
It found that only 16% could successfully differentiate between a real video and an imitation. 
The rest either got it wrong or were unsure. 
“This is significantly less than our study that we did only two years ago,” said UVU researcher Kaye Banner in a presentation of the findings. 
The improving technology has implications for elections and politics. 
Part of the survey consisted of real and AI-generated political information, like a ballot initiative. 
Banner said deepfake media influenced the opinions of potential voters “just as much” as the real media used in the survey. 
“We found no statistically significant difference in the opinion changes of people who saw a real versus a synthetic video,” she said, “this shows just how successful deepfake disinformation operations could be.” 
In 2024, New Hampshire voters received AI-generated robocalls impersonating former President Joe Biden, telling them not to vote in the primaries. 
That same year, tech trillionaire Elon Musk shared an AI-generated video of former vice president and presidential candidate Kamala Harris calling herself a “diversity hire.” 
President Donald Trump has also frequently posted AI-generated images and videos of himself on social media. 
The technology has surfaced in Utah politics, too. 
During the 2024 governor's race, a video appeared to show Gov. 
Spencer Cox admitting to forging signatures in a previous election he won. 
Though quickly debunked, it intensified a campaign already dominated by Phil Lyman's allegations of election fraud. 
It also helped spur the partnership that led to the UVU study. 
Brandon Amacher, the director of the university’s Emerging Tech Policy Lab for the Intermountain Intelligence, Industry and Security Consortium, said the technology used to make such content has only gotten better and more accessible. 
“Our students built deepfakes convincingly enough to get these results in a few days,” he said. 
“If a small university lab can do that, this is no longer a capability that is constrained to highly sophisticated malicious actors. 
It's available to anyone.” 
Amacher explained that there are several misconceptions about who is vulnerable to deepfake technology. 
It’s no longer just older generations or the less technologically savvy falling victim. 
“Democrats, Republicans and independents all detected defects at nearly identical, and I will add, uniformly poor rates. 
Somewhere between 15% and 19%, and older and younger participants performed at about the same rate,” he said. 
Despite the current administration's vocal push to advance AI technology, Utah is making moves to regulate it. 
A new Utah law requires websites and social media platforms to remove non-consensual AI-generated explicit images. 
Once reported, platforms have 48 hours to make reasonable efforts to take the content down. 
The law’s focus is on “intimate” images, but it adds to the governor’s calls for more state regulation. 
Amacher said he believes one of the most crucial takeaways is that anyone can make a convincing deepfake. 
He emphasised that his study used publicly available tools on an “old student-owned laptop.” 
“The first misconception is that this is a fringe problem, something rare and isolated, which has not or could not actually impact voter opinion or the outcome of an election. 
The data does not support that.” 
Article reasoning-pattern comparisonThis article: 6.7%Hugo Rikard-Bell: 3.1%KUER: 2.8%Confirmation Bias6.7%This article: 0.0%Hugo Rikard-Bell: 1.2%KUER: 1.3%Anchoring Bias0.0%This article: 8.1%Hugo Rikard-Bell: 3.5%KUER: 3.4%Availability Heuristic8.1%This article: 7.6%Hugo Rikard-Bell: 1.2%KUER: 1.2%Representativeness Heuristic7.6%This article: 0.0%Hugo Rikard-Bell: 0.5%KUER: 0.5%Hindsight Bias0.0%This article: 5.8%Hugo Rikard-Bell: 2.9%KUER: 1.9%Overconfidence Bias5.8%This article: 3.7%Hugo Rikard-Bell: 7.2%KUER: 7.4%Framing Effect3.7%This article: 0.0%Hugo Rikard-Bell: 1.4%KUER: 1.3%Loss Aversion0.0%This article: 2.6%Hugo Rikard-Bell: 1.1%KUER: 1.2%Status Quo Bias2.6%This article: 0.0%Hugo Rikard-Bell: 0.2%KUER: 0.3%Sunk Cost Effect0.0%This article: 6.6%Hugo Rikard-Bell: 4.0%KUER: 4.4%Optimism Bias6.6%This article: 6.6%Hugo Rikard-Bell: 2.4%KUER: 2.4%Pessimism Bias6.6%This article: 18.2%Hugo Rikard-Bell: 5.4%KUER: 6.3%Negativity Bias18.2%This article: 0.0%Hugo Rikard-Bell: 4.2%KUER: 2.2%Self-Serving Bias0.0%This article: 0.0%Hugo Rikard-Bell: 0.9%KUER: 0.9%Fundamental Attribution Error0.0%This article: 0.0%Hugo Rikard-Bell: 0.2%KUER: 0.2%Actor-Observer Bias0.0%This article: 2.6%Hugo Rikard-Bell: 1.4%KUER: 1.9%In-Group Bias2.6%This article: 0.0%Hugo Rikard-Bell: 0.6%KUER: 0.6%Out-Group Homogeneity Bias0.0%This article: 0.0%Hugo Rikard-Bell: 2.0%KUER: 2.3%Halo Effect0.0%This article: 0.0%Hugo Rikard-Bell: 0.1%KUER: 0.1%Horn Effect0.0%This article: 0.0%Hugo Rikard-Bell: 0.0%KUER: 0.0%Dunning-Kruger Effect0.0%This article: 9.5%Hugo Rikard-Bell: 0.9%KUER: 1.2%Recency Bias9.5%This article: 0.0%Hugo Rikard-Bell: 0.1%KUER: 0.3%Primacy Effect0.0%This article: 0.0%Hugo Rikard-Bell: 0.1%KUER: 0.1%Blind-Spot Bias0.0%This article: 0.0%Hugo Rikard-Bell: 1.1%KUER: 0.6%Ad Hominem0.0%This article: 0.0%Hugo Rikard-Bell: 0.5%KUER: 0.4%Straw Man0.0%This article: 4.6%Hugo Rikard-Bell: 3.6%KUER: 4.7%Appeal to Authority4.6%This article: 5.4%Hugo Rikard-Bell: 2.8%KUER: 1.7%False Dilemma5.4%This article: 0.0%Hugo Rikard-Bell: 1.2%KUER: 1.1%Slippery Slope0.0%This article: 0.0%Hugo Rikard-Bell: 0.2%KUER: 0.2%Circular Reasoning0.0%This article: 17.9%Hugo Rikard-Bell: 4.7%KUER: 4.1%Hasty Generalization17.9%This article: 2.6%Hugo Rikard-Bell: 0.3%KUER: 0.2%Red Herring2.6%This article: 0.0%Hugo Rikard-Bell: 0.5%KUER: 0.7%Bandwagon0.0%This article: 4.9%Hugo Rikard-Bell: 6.7%KUER: 5.6%Appeal to Emotion4.9%This article: 9.5%Hugo Rikard-Bell: 0.9%KUER: 0.7%Begging the Question9.5%This article: 5.7%Hugo Rikard-Bell: 2.2%KUER: 2.4%Post Hoc (False Cause)5.7%This article: 0.0%Hugo Rikard-Bell: 0.2%KUER: 0.1%Tu Quoque0.0%This article: 0.9%Hugo Rikard-Bell: 0.5%KUER: 0.4%Burden of Proof0.9%This article: 0.0%Hugo Rikard-Bell: 0.2%KUER: 0.2%Appeal to Nature0.0%This article: 0.0%Hugo Rikard-Bell: 0.2%KUER: 0.3%Composition/Division0.0%This article: 14.7%Hugo Rikard-Bell: 3.2%KUER: 3.1%Anecdotal14.7%This article: 4.6%Hugo Rikard-Bell: 0.2%KUER: 0.1%No True Scotsman4.6%This article: 15.9%Hugo Rikard-Bell: 2.3%KUER: 1.5%Ambiguity (Equivocation)15.9%This article: 0.0%Hugo Rikard-Bell: 0.0%KUER: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Hugo Rikard-Bell: 0.3%KUER: 0.2%Middle Ground0.0%This article: 0.0%Hugo Rikard-Bell: 0.2%KUER: 0.1%Personal Incredulity0.0%This article: 0.0%Hugo Rikard-Bell: 0.3%KUER: 0.1%Special Pleading0.0%This article: 0.0%Hugo Rikard-Bell: 0.1%KUER: 0.2%Genetic Fallacy0.0%This article: 0.0%Hugo Rikard-Bell: 1.2%KUER: 0.8%Unattributed Quote0.0%This article: 0.0%Hugo Rikard-Bell: 0.6%KUER: 0.7%Quote-first Misdirection0.0%This article: 4.9%Hugo Rikard-Bell: 2.6%KUER: 2.2%Biased Writer Voice4.9%This article: 0.9%Hugo Rikard-Bell: 1.1%KUER: 1.6%Indoctrination0.9%This article: 0.0%Hugo Rikard-Bell: 1.4%KUER: 0.8%Politically Left Leaning Bias0.0%This article: 0.0%Hugo Rikard-Bell: 0.4%KUER: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Hugo Rikard-Bell: 0.3%KUER: 1.0%Attempt to Sell a Product or S…0.0%

654 words analyzed.

Speakers

4speakers51%attributed speech322writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 6 words • 100.0% coverageUtah Valley University • 7 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageUtah Valley University • 23 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageKaye Banner • 25 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageKaye Banner • 21 words • 0.0% coverageKaye Banner • 32 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageBrandon Amacher • 34 words • 0.0% coverageBrandon Amacher • 16 words • 0.0% coverageBrandon Amacher • 22 words • 0.0% coverageBrandon Amacher • 4 words • 0.0% coverageBrandon Amacher • 14 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageBrandon Amacher • 17 words • 0.0% coverageBrandon Amacher • 18 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageUtah • 16 words • 0.0% coverageUtah • 15 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageBrandon Amacher • 18 words • 0.0% coverageBrandon Amacher • 14 words • 0.0% coverageBrandon Amacher • 30 words • 0.0% coverageBrandon Amacher • 6 words • 0.0% coverage
Selected voice

Kaye Banner

100%flagged-word coverage
78 attributed words23% of attributed speech87% writer coverage
0%22.5%45.0%Biased Writer Voice+41.0 ptsWriter: 0.0%Kaye Banner: 41.0%41.0%Indoctrination-1.9 ptsWriter: 1.9%Kaye Banner: 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.