The pope didn’t rock Balenciaga, and there was no moon landing conspiracy. However, in recent months, hyper-realistic images produced by artificial intelligence have gone viral, blurring the lines between fact and fiction. To combat this confusion, numerous companies now offer detection services to distinguish real images from those created by computers. These tools use advanced algorithms to analyze content and identify subtle indicators that differentiate computer-generated images from those made by human photographers and artists. However, tech leaders and misinformation experts express concern that A.I. advancements will always outpace detection tools. To assess the effectiveness of current A.I. detection technology, The New York Times conducted a test using over 100 synthetic and real images. The results reveal that while these services are advancing rapidly, there are still instances where they fall short. For example, a synthetic image of Elon Musk hugging a lifelike robot managed to deceive several A.I. detectors. These detectors, both paid ones like Sensity and free ones like Umm-maybe’s A.I. Art Detector, rely on sophisticated algorithms that search for patterns in pixel arrangement, such as sharpness and contrast, to identify A.I.-generated images. However, they do not consider contextual clues, leading to their failure in recognizing the presence of a lifelike automaton alongside Mr. Musk as highly unlikely. Some companies, including Sensity, Hive, and Inholo (the creators of Illuminarty), acknowledged the test results and affirmed their commitment to constantly improving their systems to keep up with A.I.-image generation advancements. On the other hand, Umm-maybe and Optic did not comment on the test results. The Times collected A.I. images from artists and researchers familiar with various generative tools, including Midjourney, Stable Diffusion, and DALL-E, alongside real images from their photo archive for the test. A.I. detection technology has been hailed as a way to mitigate the harm caused by A.I. images. However, experts like Chenhao Tan express skepticism, believing that A.I. will eventually be able to recreate any special human abilities with images, making it difficult to distinguish between real and fake. Concerns have primarily focused on hyper-realistic portraits and their potential deception in various contexts, including politics. Many A.I. detection companies recognize the imperfections of their tools and warn of an ongoing technological arms race. They must continually improve their discriminators to counter new and improved generators. Even when the detectors mistakenly label an obviously fake image as real, it illustrates a drawback of the technology. Detecting altered or low-quality images remains a challenge for A.I. detectors due to the alteration of important pixels containing clues about the image’s origins. Additionally, as images circulate online, they undergo various alterations, making it harder for detectors to rely on the signals they analyze. The risk of falsely labeling genuine images as A.I.-generated poses a significant problem for A.I. detectors. For example, Sensity successfully labeled most A.I. images as artificial, but incorrectly labeled numerous real photographs. This risk also extends to artists who may be wrongly accused of using A.I. tools in their work. Despite these challenges, A.I. detection companies claim their services promote transparency and accountability in various aspects, such as combatting misinformation, fraud, nonconsensual pornography, and artistic dishonesty. However, experts warn that financial markets and voters remain susceptible to A.I. trickery. To overcome the limitations of current detection methods, companies are exploring new approaches, such as evaluating perspective, limb size, and other factors beyond pixel analysis to identify A.I. usage in images.
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