IMG

Text-to-image models, like Midjourney and DALL-E, have been shown to reinforce harmful biases, often perpetuating outdated and discriminatory stereotypes. In this study, we delve into a particularly insidious bias largely overlooked in generative image research: Brilliance Bias. By age six, many children begin to internalize the damaging notion that intellectual brilliance is a male trait—a belief that persists into adulthood. Our findings demonstrate that popular image AI models possess this bias, further entrenching the misguided notion that exceptional intelligence is inherently male.

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The dataset is named Chinese rose disease dataset, including healthy leaves, black spot leaves, powdery mildew leaves and downy mildew leaves. All images in this dataset were collected from Nanyang City, Henan Province, China. And  all images were collected under natural conditions in order to ensure the true execution of the images. To improve the image variety, we randomly enhance the images in the dataset by flipped, changed the brightness, added Salt and pepper noise and added Gaussian noise.

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