You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors

Understand

Blind spots or outright deceit can bedevil and deceive machine learning models.

• Unidentified objects such as digital "stickers," also known as adversarial patches, can fool facial recognition systems, surveillance systems and self-driving cars.
• Fortunately, most existing adversarial patches can be outwitted, disabled and rejected by a simple classification network called an adversarial patch detector, which distinguishes adversarial patches from original images.
• An object detector classifies and predicts the types of objects within an image, such as by distinguishing a motorcyclist from the motorcycle, while also localizing each object's placement within the image by "drawing" so-called bounding boxes around each object, once again separating the motorcyclist from the motorcycle.