Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer

Area of Science:

• Computer Vision
• Artificial Intelligence
• Deep Learning

Background:

• Deep neural networks (DNNs) are vulnerable to adversarial attacks in both digital and physical environments.
• Existing adversarial camouflage textures prioritize attack effectiveness over stealth, appearing unnatural to human observers.
• This lack of stealth limits the practical application of adversarial attacks in real-world scenarios.

Purpose of the Study:

• To develop adversarial camouflage textures that are effective against DNN object detectors and inconspicuous to human observers.
• To enhance the stealthiness of adversarial attacks by integrating a style transfer module into the generation framework.
• To create adversarial textures that are robust in both digital and physical domains.

Main Methods:

• Proposed a novel framework incorporating a style transfer module for adversarial texture generation.
• Utilized style loss calculations to guide texture generation, ensuring visual coherence with the surrounding environment.
• Evaluated the generated adversarial camouflage textures in both digital simulations and physical-world experiments.

Main Results:

• The proposed method successfully generated adversarial camouflage textures with high stealthiness.
• The textures effectively fooled advanced DNN object detectors in both digital and physical settings.
• The adversarial textures were inconspicuous to human observers in specific, targeted scenes.

Conclusions:

• The integration of style transfer significantly improves the stealthiness of adversarial camouflage textures.
• The developed textures offer a promising solution for evading DNN-based object detection while maintaining visual subtlety.
• This approach advances the practical applicability of adversarial attacks in real-world computer vision systems.