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

TL;DR

This paper introduces a novel low-detectability adversarial patch that effectively fools object detectors while resisting detection, using geometric primitives and weighted loss functions, demonstrated on COCO and D2-City datasets.

Contribution

The paper proposes a new low-detectable adversarial patch for object detectors that is harder to identify and can bypass existing detection defenses.

Findings

LDAP effectively fools object detectors.

LDAP resists existing adversarial patch detectors.

Experimental validation on COCO and D2-City datasets shows strong attack performance.

Abstract

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. To train detectors even better, however, we need to keep subjecting them to confusing or deceitful adversarial patches as we probe for the models' blind spots. For such probes, we came up with a novel approach, a Low-Detectable Adversarial Patch, which attacks an object detector with small and texture-consistent adversarial patches, making these adversaries less likely to be recognized. Concretely, we use several geometric primitives to model the shapes and positions of the patches. To enhance our attack performance, we also assign different weights to the bounding boxes in terms of loss function. Our experiments on the common detection dataset COCO as well as the driving-video dataset D2-City show that LDAP is an effective attack method, and can resist the adversarial patch detector.