Already, deepfake videos like deeptomcruise, created with the help of AI and machine learning algorithms, are incredibly convincing (hint: it’s not actually Tom Cruise).Ī study this year in the journal Nature about the rise of misinformation online, found that people are more focused on sharing what they think will boost their social status than in sharing what is true. One expert quoted in Nina Schick’s book Deepfakes, The Coming Infocalypse, estimates that synthetic video may account for as much as 90% of online video in just three to five years – meaning it will be generated partially or entirely by artificial intelligence (AI), not humans. There could be 100 times more visual content by 2027, according to one study. Now, the same powerful and easy-to-use tools used to make and share legitimate content are also deployed to create and spread disinformation or misinformation. ![]() In an increasingly fragmented media landscape, we are witnessing extraordinary challenges to trust in media. Our inability to distinguish fantasy from reality in digital images is a wakeup call. He wanted to see how far he could get “before the guards woke up. His point was clear: If fake images can dupe the pros, imagine how hard it is for the rest of us to know what’s authentic. That is, until Bendiksen himself pointed out the fraud using a Twitter account he created using a pseudonym. Gender- and age-related differences concerning T2* and serum ferritin levels were found in the liver and spleen, but not in the pancreas.The case for content authenticity in an age of disinformation, deepfakes and NFTsĭid you hear about the award-winning documentary photographer from an esteemed photo agency whose faked images of North Macedonia’s industrial cityscapes tricked even the skilled eyes of the experts at a recent French photojournalism festival?ĭespite the random computer-generated bears that Jonas Bendiksen added to his pictures, nobody noticed his photos had been intentionally altered with computer software. Using a fast quantitative T2* magnetic resonance imaging technique, it was possible to gain insights into the iron metabolism of a healthy cohort. ![]() For women, a statistically significant age-dependent increase was found for splenic T2* values. In contrast, no such relationship was found for pancreatic T2* (r = -0.15). A significant negative correlation was found between hepatic T2*, splenic T2*, and serum ferritin (r = -0.62 liver, r = -0.64 spleen P < 0.0001). For the pancreas, these differences could not be found. A gender-related analysis revealed significant higher hepatic and splenic T2* values for women than for men (P < 0.01). Measurement of T2* was feasible in all volunteers. The correlation of organ T2* with serum ferritin and anthropometric data (age, gender, body mass index) was investigated. To assess T2* values of the liver, pancreas, and spleen, T2* maps were calculated. A multislice fat-saturated breath-hold 2D multiecho gradient-echo sequence was applied for T2* measurement. Age ranged from 20 to 70 years (mean age, 47.9 +/- 11.4 years). ![]() One hundred twenty-nine healthy subjects (85 women, 44 men) were examined on a 1.5-T magnetic resonance whole-body unit. In addition, the relationship of T2* between the 3 organs was investigated. To assess T2* values of liver, pancreas, and spleen in a healthy cohort and to compare the gained values with serum ferritin levels and anthropometric data.
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