Summary of Environmental Matching Attack Against Unmanned Aerial Vehicles Object Detection, by Dehong Kong et al.
Environmental Matching Attack Against Unmanned Aerial Vehicles Object Detection
by Dehong Kong, Siyuan Liang, Wenqi Ren
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes Environmental Matching Attack (EMA), a novel approach to generate adversarial patches for Unmanned Aerial Vehicles (UAVs) that appear natural while maintaining high attack success rates. Traditional algorithms prioritize patch effectiveness over naturalness, neglecting the importance of environmental consistency. EMA addresses this by optimizing an adversarial perturbation patch that initializes at zero, allowing the model to balance attacking performance and naturalness. The method exploits strong prior knowledge from a pretrained stable diffusion model to guide optimization and adjust contrast and brightness for better environmental matching. Experiments on DroneVehicle and Carpk datasets demonstrate that EMA achieves nearly identical digital attack performance as baseline methods, surpasses them in physical scenarios, and significantly outperforms in terms of naturalness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making fake images that can trick object detection systems used in drones. These systems are vulnerable to attacks that make the system think something is there when it’s not. The researchers found that existing methods for creating these fake images don’t take into account how they will look to humans. They propose a new method called Environmental Matching Attack (EMA) that makes the fake images blend in better with the real environment. This is achieved by adjusting the color, brightness, and contrast of the image to make it look more natural. The results show that EMA can create fake images that are almost indistinguishable from reality while still being able to trick the object detection system. |
Keywords
» Artificial intelligence » Diffusion model » Object detection » Optimization
