Adversarial clothing, a niche fashion area designed to confuse AI-powered cameras and facial recognition software, is gaining attention as privacy concerns grow. This fashion uses carefully designed patterns, colors, shapes, or materials to interfere with computer vision systems, potentially making it struggle to detect a person, identify a face incorrectly, or produce false detections. The concept originates from machine learning, where an "adversarial example" is an input deliberately altered to make an AI system misclassify something. Designers have applied this to fabric, with some garments using patterns that resemble visual features a facial recognition system might interpret as faces. For instance, Urban Privacy's FACEPTION collection uses repeated face-like shapes to create multiple false positives for facial-recognition software, and some designs use wider silhouettes to make gait-based identification more difficult. Other approaches involve infrared light or reflective materials, such as an LED coat designed to interfere with night-vision cameras. The design must be created with a particular kind of machine vision in mind. While it may feel like a new trend, designers and researchers have been exploring anti-surveillance fashion for years, but the current attention is growing as AI-powered cameras become more common.