TikTok’s ‘bold glamour’ under fire for perpetuating unrealistic beauty standards

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It’s no surprise that the ‘Bold Glamour’ filter on TikTok has come under fire for perpetuating unrealistic beauty standards. As ‘realistic’ as the filter looks (it doesn’t jump around, and the effect looks like it could be the real appearance of the user) the filter totally alters the face of users, giving the user a chiseled, smoother, and brighter complexion.
This filter reportedly uses AI to alter facial features and simulate a ‘soft glam makeup look’ that smoothes out the skin and makes it glow, darkens and thickens the brows, lengthens the lashes and makes them thicker, enlarges the eyes, contours the nose and cheeks, and plumps up the lips.
The result is so impressive that some users have started to feel uneasy about its ability to create unrealistic beauty standards, while other users are unimpressed with the filter, demonstrating that they can achieve a similar look just by using makeup.
What sets Bold Glamour apart from other beauty filters on TikTok, is its ability to blend flawlessly into the user’s face. It even detects masculine features, and adjusts or removes makeup accordingly, which has already sparked a controversy from female and male users who don’t typically have features associated to their gender.
The filter has gone viral since its release on TikTok, and users are divided. While some are impressed with the filter’s realism, others are concerned about its potential impact on self-esteem and self-image, with some expressing their concerns that filters like Bold Glamour could prompt users to seek actual cosmetic surgery to achieve their filtered look in real life.
The concern does carry weight. The American Journal of Cosmetic Surgery recently published a study that found a significant association between the use of face-altering software on social media and the desire to undergo facial cosmetic procedures.

Despite attempts by The Verge to uncover the ‘flawless’ technology behind Bold Glamour, TikTok has remained tight-lipped.

However, augmented reality consultant Luke Hurd has suggested that the filter may be using machine learning technologies like Generative Adversarial Networks (GANs). These networks put competing neural networks against each other until the filter lands on a way to combine the user’s actual face with the ideal version created by the filter.